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HBR IdeaCast podcast series

4 Business Ideas That Changed the World: Scientific Management

A roundtable conversation on Taylorism and how it shapes management still today.

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In 1878, a machinist at a Pennsylvania steelworks noticed that his crew was producing much less than he thought they could. With stopwatches and time-motion studies, Frederick Winslow Taylor ran experiments to find the optimal way to make the most steel with lower labor costs. It was the birth of a management theory, called scientific management or Taylorism.

Critics said Taylor’s drive for industrial efficiency depleted workers physically and emotionally. Resentful laborers walked off the job. The U.S. Congress held hearings on it. Still, scientific management was the dominant management theory 100 years ago in October of 1922, when Harvard Business Review was founded.

It spread around the world, fueled the rise of big business, and helped decide World War II. And today it is baked into workplaces, from call centers to restaurant kitchens, gig worker algorithms, and offices. Although few modern workers would recognize Taylorism, and few employers would admit to it.

4 Business Ideas That Changed the World is a special series from HBR IdeaCast . Each week, an HBR editor talks to world-class scholars and experts on the most influential ideas of HBR’s first 100 years, such as disruptive innovation, shareholder value, and emotional intelligence.

Discussing scientific management with HBR senior editor Curt Nickisch are:

  • Nancy Koehn , historian at Harvard Business School
  • Michela Giorcelli , economic historian at UCLA
  • Louis Hyman , work and labor historian at Cornell University

Further reading:

  • Book: The One Best Way: Frederick Winslow Taylor and the Enigma of Efficiency ,  by Robert Kanigel
  • Case Study: Mass Production and the Beginnings of Scientific Management , by Thomas K. McCraw
  • Oxford Review: The origin and development of firm management , by Michela Giorcelli
  • Book: The Principles of Scientific Management , by Frederick Winslow Taylor

CURT NICKISCH: Welcome to 4 Business Ideas That Changed the World , a special series of the HBR IdeaCast. In 1878, a machinist at a Pennsylvania steelworks noticed that his crew was not producing nearly as much as he thought they could. Frederick Winslow Taylor began systematic studies to determine exactly how much work should be done. With stopwatches and later stop-motion film, Taylor analyzed the efficiency of workers, tweaking everything down to how they moved their arms, the size of their shovels, and how long they could take a breather. It helped factory owners make more pumps, steel, and ball bearings with lower labor costs. It was the birth of a management theory… called scientific management or Taylorism. And Taylor became the face of it, a world-renown management consultant before there were any. Critics said his drive for industrial efficiency depleted workers physically and emotionally. Congress held hearings on it. Still, scientific management was the dominant management theory 100 years ago in October of 1922, when Harvard Business Review was founded. It spread around the world, fueled the rise of big business, and helped decide World War II. And today it is baked into workplaces from call centers to restaurant kitchens, gig worker algorithms, and offices. Though few of us would recognize it and few employers would admit to it. In this special series from HBR IdeaCast , we’re exploring 4 Business Ideas that Changed the World . Each week, we talk to scholars and experts on the most influential ideas of HBR’s first 100 years, such as disruptive innovation, shareholder value, and emotional intelligence. This week: “Scientific Management.” With me to discuss it are Nancy Koehn, historian at Harvard Business School. Michela Giorcelli, an economic historian at UCLA. And Louis Hyman, a work and labor historian at Cornell University. I’m Curt Nickisch, a senior editor at Harvard Business Review and your host for this episode. Nancy, let’s start with you. How were workers managed at the time that Taylor joined the workforce in 1878?

NANCY KOEHN: That’s a great question. And the answer is all over the map. That is, how workers were managed and what their experience of working was in 1878, varied enormously, by industry, by place, by tradition, which still had a very big role to play in how workers and management came together to produce a good or a service. Although it was, by far in a way, about goods in the late 19th century in America. So, you had people like, in the early years of the steel business, an industry that Taylor will get into. Trying to figure out how, as they learned that making more steel makes the price of each unit of steel go down. In other words, they stumble into economies of scale. And they’re struggling to figure out, well, what does that mean for how we put men, mostly men in the steel business, together with capital? You have these different evolving, often chaotic arrangements. So, when we think of, you know, high-efficiency factory production today, we, we don’t have any, any inkling into what it was like in the late 19th century, to be in a factory because it was much, much more learning by doing, and much more disorganized than when we think of, say, semiconductor production today.

CURT NICKISCH: Louis, at the time, what was the understanding of being productive, of productivity?

LOUIS HYMAN: Well, I’m just going to echo Nancy here, that we think of productivity today as, how much stuff could I make? How efficient am I? Well, these ideas are not ahistorical. They’re grounded in a particular set of values that comes out of the transition from working in a shop of an apprentice system to a world where you are working in a factory for a boss. That is the emergence of wage work. And it’s not just technology that changes, which we’re all very familiar with, but social relationships that we go from a place where the apprentice and the master, in a sense, the master of a craft like a cobbler work side by side to produce a few high-quality shoes every day, to a world where a wage worker wants to produce as many shoes, as possible of an uncertain quality. So, workers themselves, as they are apprentice and masters imagine that, why shouldn’t I drink beer and sing songs while I make my shoes? This is quite different than the world of a factory, where Taylor exists.

CURT NICKISCH: Michela, can you develop that further? It’s, it’s hard to imagine for us today, right, a time when productivity wasn’t even an economic principle.

MICHELA GIORCELLI: It definitely is, but as Louis just pointed out, despite its centrality in the modern debate, productivity is a fairly recent concept. Businesses were very small. They would average three to four workers. It was very easy for the owner to coordinate their task, to monitor their jobs. And very easy, owners and employees that were working side by side to produce output. The situation traumatically changed with the industrial revolution because the dynamic of the workplace was completely changed. Let’s think, for instance, the company is building railroads and telegraphs. At that point, it became extremely important to assign the best task workers, in order to coordinate production across different units and in different parts of the country. As such, the development of the concept of productivity is strongly related to the development of the concept of management. Intended as a bundle of practices, that coordinate the tasks and the work of the employees, in order to reach the optimum productivity.

CURT NICKISCH: So, this is the business world that Taylor came into. Nancy, who was Frederick Winslow Taylor? And what did he experience in his first job?

NANCY KOEHN: So, Frederick Winslow Taylor was the son of Quakers. His father was a successful lawyer, who actually had made enough money, um, that he could live a kind of life of leisure. And his mother, a woman named Emily Annette Taylor, a direct descendant of Mayflower voyagers, way back in the 17th century. She was also an ardent abolitionist and suffragette. So, he comes from this, again, patrician family with, you know, a very active mother. And you know, this is a young man who had nightmares, as a boy, invents a machine, a set of harnesses to wake him up when he starts to turn so he doesn’t have nightmares.

CURT NICKISCH: Hmm.

NANCY KOEHN: This is a young man who before he goes to a party, makes a list of all the attractive girls and the unattractive girls, and resolves to spend equal time with both. This is a young man when he plays croquet says, “Oh, here’s the geometry of this particular croquet field. And here are the kind of vectors, I wanna be able to hit, to win the game.” I mean, he’s, he’s interested in control, which is an important aspect of scientific management. He passes the Harvard admissions exams with some m- room to spare, but he has these terrible headaches and real eye problems. And decides not to enroll in college. And instead, he takes a job as a worker, he later will kinda rise to management, in Philadelphia, in what today we call a machine tool company. It’s called Enterprise Hydraulics, and it makes pumps. And, and he, he begins to think then, about how do you increase efficiency in labor’s relationship to management, and in labor’s relationship to the machines or the tools they use, as part of their role in increasing productivity.

CURT NICKISCH: What did he see there, at work? And, you know, what did he end up doing about the problems that he solved?

NANCY KOEHN: Well, he sees that, that workers are in his eyes, not working as hard, as they can. And he, he becomes interested in how do I kinda tease out that problem, right, unpack it and what do we do about it. Most workers, including the apprentices that Louis was talking about, are paid based on what they make, or, or how much they make. So, in that kind of system, workers are trying to, you know, do more. But ultimately, in almost every kind of piece rate or pay from what today, an economist like Michela would call pay for workers marginal product, in that setting, almost all managers said, “Well, after a certain point, you’re not gonna get any more.” So, there’s, if you will, a kind of pay ceiling. Well, workers figured that out real quick and decide, Well, I’m only gonna work as hard as I need to work in order to make the maximum that my boss will pay me. And that then, presents a really interesting problem for Frederick Taylor which is, how do I get workers to work more? So, that’s part of the problem. Workers aren’t working as hard, as they can. And they’re not necessarily working in a standardized way. And that was true in the way that you heard Louis speaks so eloquently about, shops and apprenticeships and small-scale manufacturing. And even, remember, in America, a lot of America is still moving from the farm to the factory. So, you have people that never worked indoors before, in a sense. Adding to, if you will, the uncertainty and the caprice and the variation that Frederick Taylor sees. And that makes him anxious and determined to clean things up.

CURT NICKISCH: So, he starts conducting experiments to better control what workers are doing. Is that right?

NANCY KOEHN: That’s exactly what he starts doing, right? And he comes up with all kinds of what today, we’d call, well, we might call them standard operating practice. I was just gonna say, use the word, rules, right? Ways of doing things, um, in very specific ways of doing things. Every single job can be reduced to a series, maybe a very small number of tasks, done one right way. One right way. And he’s trying to reduce, right, the amount, if you will, the standard deviation in what each worker does in a very specific way along a very specific, what today we would call, production function.

CURT NICKISCH: What experiments is he running? What is he making workers do?

NANCY KOEHN: So, one of the things he’s doing, for example, in Midvale, where he’ll spend some real length of time. So, the famous one is a Dutchman, an immigrant laborer who, handpicked by Frederick Taylor, what he called a first-class man. And he does a series of studies about how Schmidt, which is a name he gives him in his, in his writings, moves pig iron, right. It’s not moving on a conveyor belt, he’s moving pig iron.

NANCY KOEHN: And, and by showing Schmidt how to do this, right, you, you bend down this way. You pick it up here. You take this many steps over, across the whatever, the factory floor to move it over here. And then, you rest at certain intervals. And you rest for exactly whatever, 90 seconds. By showing him exactly how to do that, according to Frederick Taylor, he increases Schmidt’s output by almost, I think it’s three and a half fold. It’s like, from 12 tons a day to something like, 47 tons of pig iron a day, that he’s moving. And how he literally dissects that all the way down to how many steps he takes, and how many times he does it before he rests for how many seconds. That is the essence of what he’s doing, for, for a myriad, scores and scores of component parts of a job.

LOUIS HYMAN: What I think an important part of what Nancy is talking about, it’s not just the imagination of work, but the imagination of the worker. What’s crucial here, is that his idea of Schmidt is an idea, and it appeals to the readers of his theory. So, he describes him, as you know a first-rate man in terms of his ability, very strong, very industrious. But also quote, “Mentally sluggish.”

NANCY KOEHN: Right.

LOUIS HYMAN: That this is someone who is not really able to solve problems for himself. Taylor writes about him, that he is so stupid that the word percentage has no meaning for him. So, it’s not simply possible to give him incentives through piece rates to make him work harder. He has to be guided by the hand of a manager.

CURT NICKISCH: Michela, Taylor’s coming up with this system then, to make workers do things a certain way. And he leaves Midvale Ironworks in 1890, and spends the next years consulting with various companies, Bethlehem Steel one of them, to help them increase productivity. He eventually even refashions himself as a management consultant – perhaps the first one ever, right?

MICHELA GIORCELLI: Yes, exactly. So, Taylor developed himself a new profession and called himself a consulting engineer in management. And in this role, Taylor ended up serving a long list of prominent firms in many industries, cities, and towns. And his main goal, when he was working with these different companies in different roles, was to develop the core ideas of the, scientific management like the idea of scientific selection of workers. And the importance of differential pay incentives, in order to motivate the workers to increase productivity. So, the fact that he spent many years consulting around the country actually helped him to put together the principles of scientific management that will become the title of his most famous book published in 1911.

CURT NICKISCH: Mm-hmm. Louis, how did workers feel about Taylor’s methods?

LOUIS HYMAN: Not good, Curt, not good. It was an incredibly exhausting way to work with somebody else telling you what to do all day, how to move your body.

CURT NICKISCH: Having somebody stand there with a stopwatch.

LOUIS HYMAN: No, you don’t feel like a man. You feel like a dog, right? You are being inspected constantly. And it is very hard to feel good about what you do, and you’re listening to his watch rather than your body over when you’re tired. And maybe your wages go up, maybe they go up 50% and your productivity goes up 250%. But ultimately, you don’t care because it’s not just about that one day of lugging pig iron, this is your whole life.

CURT NICKISCH: Hmm. Nancy, how did factory managers and owners that Taylor worked with, feel about him and his results?

NANCY KOEHN: So, the answer is very much mixed in terms of how managers and firm owners reacted to Taylor. There was a personal piece, which was he was, I think autocratic and very, very convinced. I mean, there’s something very naively utopian about Frederick Taylor. He thought was gonna build a world in which there was so much surplus created by all this increased labor productivity, that there would be no reason to fight about the surplus. He, he felt this was gonna be such a benefit to everyone concerned, that he could never understand why not only workers, but firm owners and managers who didn’t always welcome his, you know, it was either my way or the highway with Frederick Taylor, or Fred Taylor.

NANCY KOEHN: And I think both, in terms of his attitude and in terms of his didactic sense of, this is the way we’ll do it, he confused and he angered a variety of different kinds of managers. Particularly foremen, but also firm owners. He really was certain that there was one right way, and it was his way.

CURT NICKISCH: Mm-hmm. So, somehow, despite all this resistance, both from workers and some of the people who employed him, this method ends up becoming a movement. Michela, when did scientific management start attracting followers, outside just the, you know, word of mouth work that Taylor was getting here and there, at different companies?

MICHELA GIORCELLI: The first, the large-scale diffusion, up in 1903, when Taylor presented the first paper at the American Society of Mechanical Engineers annual conference. In the following years, this was between 1904 and 1912. Taylor devoted his time and his money to promote and diffuse the principle of scientific management. He traveled a lot around the country, giving lectures in university, talking at professional societies. And in this way, the ideas of Taylorism start spreading in the US. However, the turning point happened in 1910, when there was an Interstate Commerce Commission hearing and one of the attorneys argues that the U.S. railroads could have saved up to $1 million a day, if they introduced the scientific management principle. That hearing was extremely popular at the time, widespread coverage in the newspapers. Taylor’s scientific management ideas were on every lip. And the idea of efficiency, in a way – the productivity drive that is one of the core characteristics of the U.S. business model in the 20th century – starts becoming a national idea.

CURT NICKISCH: Nancy, right around this same time, workers go on strike at an arsenal, just outside Boston, to protest Taylor’s methods. Fun fact, Harvard Business Review was actually headquartered there at the Arsenal. I interviewed there, when I got this job. What happened at that strike?

NANCY KOEHN: So, Taylor sent one of his disciples to institute basically, time motions studies. And he shows up with a stopwatch. And he starts timing different workers doing different things. Clicking the stopwatch, and you know, I’m sure he’s got a clipboard and he’s writing things down. One worker says, “I won’t let you time me.” And management immediately fires him because management is interested in what Taylor’s work can bring to productivity at the arsenal. So, the worker is fired on the spot. And then, all the other workers just walk off the job and strike. And so, it’s a very good example of the assumption that there’s one right way, that only a certain small group of people called managers and scientific management experts – today we might call them consultants – that only small group of elite folks have that one right way. And that they have the power to put that one right way in place, regardless of the experience it offers for workers. And again, you think about the suddenness of this transition for many, many workers between 1880 and 1920, coming literally in many cases, off a vessel from Europe or some other part of the world as immigrants, and moving into factories. And the abruptness, right, and the, the massive discrepancy in power, the idea that what you know and what you’ve learned on a job isn’t worth anything if there’s only one way to do it. And the only people that can tell you that are the small group of high priests in industrial capitalism.

CURT NICKISCH: The strike got so much attention Congress investigated it.

NANCY KOEHN: Right. Congress investigates another moment for Taylorism, to take the spotlight on some kind of national stage. And on Capitol Hill, it wasn’t greeted with, you know, unconditional approval. Quite the opposite piece here, that was very, very important. A Congressman named William Wilson who is the chair of the committee that’s investigating Taylor, is worried about all the things we’ve been talking about here. Is it all about, just increasing speed? So, lots of folks on Capitol Hill, like Wilson, were concerned and so were labor leaders, about the skills that Louis was talking about, the lots of workers develop on the job in lots of different kinds of businesses and industries and production processes. What happens to that if we’re breaking down every single task into these tiny component parts and basically saying, there’s no room for any kind of discretion or experience or innovation to happen on the part of working men and women?

CURT NICKISCH: Louis, Nancy mentioned labor leaders there. How did the larger labor movement figure into this backlash?

LOUIS HYMAN: Well, I think they figured into it in the way that Nancy was talking about, as not just the question of making more widgets, moving more pig iron. But the larger political meaning of it for a democratic citizenry. Now, a long question throughout the 19th century was, how can wage work exist in a democracy? In a sense that, how can you obey for eight, 10, 12 hours a day, and then, expect to be free the rest of your time? How is it possible for someone who is so broken and dominated to then, exercise political freedom? And this is exactly what the president of the American Federation of Labor, Sam Gompers, tells congress. He says, “I grant you that if this Taylor system is put into operation, as we see it and, as we understand it, it will mean great production in goods and things. But in so far, as man is concerned, it means destruction.” And that is the question of Taylorism. Of course, you can make more stuff, but what is the cost? What is the cost in democracy? What is the cost in the long-term health of those workers? Gompers tells congress that Taylorism was the antithesis of industrial education. Because what Gompers was all about, was the idea that workers could be educated to be more productive. Why did they need those managers coming in, in with their stopwatches? Why couldn’t they themselves begin to figure out better production processes? And so, in some ways, this anticipates the insights at Toyota later in the 20th century. This kind of bottom-up worker knowledge of… obviously Gompers doesn’t call it Toyotaism. But the fundamental question for Gompers is, what are humans for? What is the range of human capacities? What is it the worth of the person, if they’re expected to become like a machine? And so, for Gompers then, productivity is not a neutral idea.

NANCY KOEHN: Yeah.

LOUIS HYMAN: But essentially about the power between workers and owners in that exact moment, but also in the future of America. For whom do the benefits of productivity flow? Does it go to the owners of capital? Does it go to the workers themselves? And I think that is the great debate, you know. Maybe I do get paid enough that I get an extra beer in the weekend. But what does that mean, if I’m so exhausted, so worn out, so, so broken, by this kind of work, that I don’t even want to leave my house on the weekend?

CURT NICKISCH: Michela, what was the upside of that congressional hearing? Did it stunt the spread of scientific management? Or was this one of those, any publicity is good publicity, sort of things?

MICHELA GIORCELLI: It was definitely one of, any publicity is good publicity. In the sense that, on paper, the committee report stated that neither the Taylor system or other management systems should impose on the workers against their will. And also, that any system of shop management, that should be the outcome of a mutual consensus between the workers and the managers. However, the committee declined to make any recommendation for this legislation. And so, and Taylor was very lucky to have the Congress come up with a very mild report. And Taylorism could continue to be spread and to be adopted, not only in the U.S., but also worldwide in the years to come.

CURT NICKISCH: Coming up after the break, we’re going to follow that spread, and discover how Taylorism got baked into our modern life and work. One hundred years later, have the human and social costs of increased productivity been resolved? Stay with us.

CURT NICKISCH: Welcome back to 4 Big Ideas That Changed the World: Scientific Management . I’m Curt Nickisch. Nancy, Taylor died in 1915, really kind of at the height of scientific management as an overt practice. This is a time when business schools were cropping up around the United States. Harvard Business Review was founded in 1922. The practice of management is taking shape and scientific management has pole position there. What effect did it have on the U.S. economy in the 20th century?

NANCY KOEHN: The British management scholar Lyndall Urwick observed that America owes to Taylor a large of incalculable proportion of the immense productivity and high standard of living that began to take hold, as the 19th century became the 20th century. I’m very skeptical of that. Scientific management took hold with, you know, corresponding larger effects in certain industries and not in, in others. You know, Taylorism didn’t really affect retailing. It really didn’t, you know, affect other industries, where labor was a very, very important piece of the story, in terms of the contribution of labor. DuPont Chemical, a huge – or Procter & Gamble, you know, a huge consumer products company, it’s not clear that Taylorism had a big effect in that company, say between the years of 1890 and 1950. It’s just, Taylorism took hold in places where labor’s contribution could be, you know, sliced into these tiny slices. Taylor played a big role there. That’s a big idea that mattered, right? But in terms of actually hiking up productivity, industry by industry, and the leading industries that created the 20th-century American economy, I think we’re on more shaky ground. Let me say one other thing, though, that’s really important to the, the power of the idea of scientific management, you know. Peter Drucker, a well-known management consultant, writer, thoughtful commentator on the evolution of business and management. Once said that Taylor was so important, he displaced [Karl] Marx in the pantheon of critical thinkers in the modern age. He included Darwin, Freud, and Marx. And he said, nope. Make way for Fred Taylor. Karl Marx goes out. I disagree with that completely, right? Karl Marx, right, understood that if Frederick Taylor would come along, commoditize labor, diminishes human creative, innovative potential, and squeeze it into a piece of a machine, and that’s what scientific management did in so many ways, subtly and less subtly. It really moved Marx’s prediction for the role of labor in industrial capitalism ahead, by leaps and bounds. He codified Marx by saying, “Labor is a commodity. We can get it to do exactly what we want. We want first-class pieces of commodity like Schmidt, and we’re gonna tell them exactly how to do things down to the second. Now, you contrast that with other kinds of productive processes, both in the Toyota system, Japanese capitalism, or German capitalism, or the beginnings of the information revolution in Silicon Valley, and the situation is completely different. And in all those, in all those instances, you have massive game-changing increases in productivity.

CURT NICKISCH: Sticking with the communists here, Louis, one surprising fan of Taylor’s ideas was the revolutionary Vladimir Lenin. Can you tell us more about that?

LOUIS HYMAN: Sure. Initially, Lenin was very skeptical of scientific management, following other kinds of labor critics that it was just a way to sweat more labor. That is to put people in sweat shots to increase their productivity, but not really pay them for the full value of that increased productivity. But he changes his mind. So, in 1917, he releases his book, The State and Revolution , which, if you’re the kind of person who is romantic about Marx, this book will not make you romantic about Lenin. So, if Marx imagines a future where we work a few hours a day, we fish a little, we do philosophy, in some sense, this is, imagining us all, as capitalists living off the prosperity. Well, this is not Lenin’s vision at all. In Lenin’s vision, he’s very much in line with Taylor’s thinking. Only, instead of management, there is the state. Lenin suggests that every worker should have six hours of physical work daily. And then, four hours of working for the state. So, a total of 10 hours. And this is a very different conception from Marx. And certainly, a different conception of what labor leaders like Gompers, want to see the future as. But it speaks to the underlying brutality and antihumanism in certain ways of Taylorism, and, of course, Leninism.

CURT NICKISCH: Well, he thought it worked, right? And he wanted to implement it, so that the Soviet Union would be competitive. Michela, we just heard about Lenin there, but how did Taylor’s idea spread outside the US?

MICHELA GIORCELLI: Taylor’s idea had two key characteristics to spread outside the US. The first one is that they were very adaptable, meaning that they were not specific to give them, from size, or a given sector. And this goes back to what we discussed before – the fact that Taylor has developed his, his ideas after widespread consulting in different industries, in different firms across the US. And the second key characteristic is that Taylor’s ideas were complemented by firm-specific practices. For instance, Taylorism was very well accepted in Japan. But the interpretation of the productivity drive in Japan was a little bit different relative to the US. The idea of increasing productivity in Japan was mostly related to the management of waste, and reducing waste, as much, as possible. And in a way, these were the first steps of lean production and the lean management system that would become predominant in Japan in the late ’60s and in the ’70s. Taylorism also spread in Europe. It ended up being adopted in many countries, including Britain and France, were the two European countries more active in the adoption of Taylorism.

CURT NICKISCH: So, was the industrial efficiency of the U.S. in World War II, did that strengthen this notion of exporting scientific management?

MICHELA GIORCELLI: Yes, absolutely. In the early ’40s, the technical and scientific knowledge of some European countries like Germany and the U.S. was very comparable. However, what was key for the U.S. to winning the war, was being able to produce at much higher speed than all the other European countries. And indeed, the U.S. invested a lot in the program for diffusion of managerial knowledge and scientific management. One of the most famous programs sponsored by the U.S. between 1940 and 1945, was managerial consulting to large U.S. companies involved in work production. After World War II, the U.S. sponsored, um, many programs to diffuse managerial technology. World War II definitely helped to create the so-called U.S. way of doing business. That was exported to Europe and Japan, in the aftermath of World War II.

CURT NICKISCH: Okay. Louis, as we move forward in the 20th century, the economy moves away from the factory and the shop floor. More service sector, more professional services. Did scientific management make that transition too?

LOUIS HYMAN: Absolutely. It has a huge shadow, a long shadow over how we think about the workplace. And this urge to quantify workers, to quantify time, existed as much, in the typing pools of words per minute, as it did in moving tons of pig iron. The movements and machines of fry cooks, as much, as textile workers. And now, of course, in the gig economy, or on bikes and cars, or on computers, where workers are constantly surveilled, treated like a commodity, watched by algorithms that are very much the descendants of Taylor’s stopwatch. And so, Taylor is everywhere. And it’s built into a kind of visceral sense of how to manage. You don’t really get an alternative in America to Taylorism until Douglas McGregor developed his famous Theory X and Theory Y. And Theory X is basically Taylor. And Theory Y is Gompers, that, that workers actually like being engaged with their work. They actually learn to take pride in their work. They respond to incentives. They can actually calculate percentages. But part of the reason why this Theory Y is possible to imagine by the 1960s, is that on the one hand, you have several generations of mass education, both in grade school and in high school. But also, the cutoff of immigrants. So, this is exactly the moment when the number of people who are born outside the U.S. is at its, its lowest point ever. So, it’s very easy to imagine other Americans like yourself, if you are a manager. And so, we see this story of who is like us and who is different than us, again, play out in this possibility of a new way to think about management. But even in those theories that are beginning to be developed in the 1960s, there is a sense that productivity remains everything.

CURT NICKISCH: Yeah. Nancy, Louis was talking there about scientific management kind of baked into contemporary offices and, and workplaces. Are we scientifically managed?

NANCY KOEHN: One of the really interesting aspects, just to get and to feed on the question what Louis just said, is how scientific management in the last 40 years has come to retailing, has come to call centers, has come to Amazon warehouses, has come to restaurants. As scientific management, as the economy has shifted, has increased its reach. Um, you see that both, in the recent unionization drives at Amazon, which have then, right, been undergirded by particular workers’ experiences, including h-

CURT NICKISCH: Right, how many times can you use the restroom?

LOUIS HYMAN: Absolutely.

NANCY KOEHN: And how much time has to elapse before you go back to the restroom, right? And how many boxes are you supposed to pack? We see it there. We see it in call centers, where if you scratch the surface of most call centers, right, which regardless of where they’re physically located, you will find people with headsets managed down to the minute. Not only in terms of bathroom breaks, but how many calls they have to handle per 15 minutes interval. It’s extraordinary. Call centers are the new, you know, Midvale Steel. So, I think that yes, I think that we, we are scientifically managed in, in many, many different kinds of work. Not all occupations are scientifically managed, but many, many of them were that weren’t, say, 60 years ago. And that speaks not only to its ability to adapt and evolve to new industries and new kinds of economic activity. It also speaks again to the huge hegemony that scientific management has had on the question of, how should workers and management do what they do together. The idea that, you know, kind of leaves us all in the dust, is Frederick Taylor’s scientific management. And that’s today, right, and it was true in 1910. And to me, that’s just so astounding. Why this answer? Why this right way? ‘Cause there isn’t one right way, and the history of capitalism shows us that. Even the history of Silicon Valley shows us that. But still, it’s scientific management that has left all kinds of other ideas, at least in America, in the dust.

CURT NICKISCH: Yeah, Michela. How is scientific management regarded today? If I use that term with people, a lot of people don’t even know it.

MICHELA GIORCELLI: Yes. Scientific management idea doesn’t have a very good perception today. In the sense that scientific management is seen as the program that denigrates the workers’ activity in order to increase productivity. But indeed, almost all the firms all over the world, adopt the scientific management principle. In the sense that, all the production is organized today, not only in the industry but also in services, is strongly shaped by the idea of productivity. And this is also testified by the increasing importance of managers, the rise of managers’ compensation that are considered key inputs for a firm, success. So, definitely the legacy of Taylor, even if maybe not properly acknowledged, is present in all the type of businesses.

CURT NICKISCH: Louis, how much do we owe our understanding of being productive and efficient and even, feeling productive or, you know, hating waste to Taylor?

LOUIS HYMAN: Well, Curt, it’s interesting. I think that the way we think about productivity is rooted in Taylor. But it’s also Taylor that roots us in a very particular conception of work. That on the one hand, there is a worker who is valuable, who is creative. This is the manager, as worker, right? This is the Silicon Valley programmer who is still lauded today. On the other hand, there is the worker who is not creative, and in sense then, not valuable. This is the person we should treat like a machine. When we look at the history of Silicon Valley, we often see the history of these technologists and coders, these creatives who play ping pong, whatever, who sit around in Bahama shorts, just not really doing anything, but then, having a great thought. But behind that-

CURT NICKISCH: And they’re drinking beer on the job, just like they did in Taylor’s time.

LOUIS HYMAN: Exactly. They did, right? But behind that is a whole world of production that gets written out of the history, you know. In the 1970s and ’80s, we hear the story of Steve Jobs and the Woz and Apple. But we hear less about the hundreds of thousands of people who actually worked in assembly plants in Silicon Valley.

NANCY KOEHN: Or China.

LOUIS HYMAN: And oftentimes, when these factories were talked about, they were talked about as robots building robots. But every time somebody said “robot,” if you actually looked at the actual people who worked there, how things were actually made in these lean production sites, it was actually women. Usually, women of color, who are usually immigrants. And so, we still have this imagination of some work being valuable, and some people being valuable. And they sort of, reinforce one another. What is the meaning of this today? Well, we are still thinking of productivity as something very bifurcated between those who, we don’t need them to be productive. They are 10X programmers. They are creative entrepreneurs. They can do amazing things in a few minutes, as long, as we give them time to think. And then, we imagine people who can’t think. People who aren’t deserving of time, people who aren’t deserving of that kind of creative human potential. For me, that is the moral meaning of productivity. This question of, who we value and what do we value?

CURT NICKISCH: Hmm. So, I want to ask each of you where scientific management leaves us, you know, today, in this world of work? What kind of future are we pointed to, now? And, I’ll go around the horn, but Nancy, maybe we could start with you.

NANCY KOEHN: So, I just want to pick up some threads, that there’s a runoff of one’s humanity in scientific management. A runoff of, you know, a giant sucking sound that says, some people, just to echo Louis, are, are more important than others. Some people make bigger contributions than others. Some work is more valued than others. And therefore, some people are more valued than others. That’s simply not, it’s just not, those are not very good eye beams to go into a century now, increasingly dominated by a- automation, artificial intelligence, and a very kind of unabashed and not terribly thoughtful embrace of all things technological. The storyline here, is not pulling from, in all kinds of directions. Not just morally, and not just in terms of political, social economic equality. And the massively destructive effects of the huge ramp-ups in inequality wealth and income we’ve seen over the last 50 years around the world. But this, even though, even holding those away. The storyline here, doesn’t look like it ends terribly well. And I think that piece, right, which Gompers, Gompers was talking about, you know, and, and so were other labor leaders in the, all throughout the first three of four decades of the 20th century. In which, a few politicians today, are talking about, that’s a very, it’s a very important nugget for all of us to chew on.

CURT NICKISCH: Michela?

MICHELA GIORCELLI: I will take a more economic perspective, here. And I see that the legacy of Taylorism has a lot to do with productivity. The idea of increasing productivity will remain with us also, in the future. It may, however, change. There are recent studies, for instance, focusing on the productivity of working from home. Or how technology allows us to work together. And we saw that during the pandemic, it allows us to increase productivity even without being physically in the same place. So, I think that the productivity is still there, help manage workers is still there. But the way in which it’s happening is changing, moving from the factory perspective, workplace perspective, to more of the work per se, no matter where it is performed.

CURT NICKISCH: Louis?

LOUIS HYMAN: Yeah. I think that this question of, what is the meaning of Taylor and productivity in the digital age, as Nancy and Michela were just saying, is the essential one. So, the question remains, as it did a century ago, who benefits from increased productivity? And in the digital era, there is again, the promise of machines continuing to liberate us from drudgery. To enable us, to become more fully human in our work. And this is important because we have a lot of challenges in the 21st century. And there’s so much talent in the world that right now, is sitting behind a cash register, making change, or more, just wrestling, hauling water back from a stream to her house. And so, we need technology to liberate us from these. And we don’t need it for workplace surveillance. So, I think the question about productivity is less about technology than the social imagination. How do we bring ourselves into this conversation about increasing our productivity, so that we can turn over that drudgery to our machines, to our computers, so that we can focus on human potential, human relationships, and human work?

CURT NICKISCH: That’s Nancy Koehn at Harvard Business School, Michela Giorcelli at UCLA, and Louis Hyman at Cornell. Next time in 4 Business Ideas That Changed the World : disruptive innovation. HBR editor Amy Bernstein will talk to three experts about how our understanding has evolved of how new entrants succeed in the marketplace – and how to hack it in your favor. That’s next Thursday right here, in the HBR IdeaCast feed after our regular Tuesday episode. This episode was produced by Anne Saini. We get technical help from Rob Eckhardt. Our audio product manager is Ian Fox, and Hannah Bates is our audio production assistant. Special thanks to Maureen Hoch. Thanks for listening to 4 Business Ideas That Changed the World , a special series of the HBR IdeaCast . I’m Curt Nickisch.

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What is Scientific Management Theory? Scientific Management Theory In A Nutshell

Scientific Management Theory was created by Frederick Winslow Taylor in 1911 as a means of encouraging industrial companies to switch to mass production. With a background in mechanical engineering, he applied engineering principles to workplace productivity on the factory floor.  Scientific Management Theory seeks to find the most efficient way of performing a job in the workplace.

Table of Contents

Understanding Scientific Management Theory

In the early 20th century, there was also a general belief that workers were lazy and inefficient.

Taylor argued that the remedy for inefficiency was to be found in systematic management – there was no use trying to recruit men who had extraordinary work ethics.

Taylor was one of the first to look at productivity from a scientific standpoint, believing in universal laws that governed labor productivity and efficiency.

For this reason, “Taylorism” is often referred to as one of the first forms of scientific management .

Taylor’s classic assumptions about workers

Taylor’s belief that workers were only motivated by money provides the basis for several classic assumptions:

  • Workers find their work unenjoyable and have a natural tendency to slack off in a process he called natural soldiering. To counter this tendency, they must be closely monitored and controlled.
  • To increase worker investment in their job, it should be broken down into bite-sized actions.
  • Training should be provided to all employees to create a standardized way of working.
  • Workers should be paid based on how much they produce (piece rate). Taylor argued that this would create a win-win scenario where the employee would earn more money and the business would maximize its profits.

The four core principles of Scientific Management Theory

Taylor was perhaps a product of his time, viewing employee labor as an extension of machine labor.

He was also a strong proponent of autocratic leadership , which an increasing number of modern companies are shying away from.

However, his principles of scientific management are still relevant today.

Here is a look at each principle:

  • Select methods backed by science

Businesses should avoid giving workers the freedom to perform their jobs in any way they see fit.

The scientific method must be used to identify the single, most efficient way of doing the job.

  • Assign workers to jobs that match their aptitude

Instead of assigning workers to jobs at random, assign them to roles where their unique capabilities will allow them to work at peak efficiency.

  • Monitor worker performance

Monitor efficiency and ensure that necessary instruction is given on how to maintain productivity.

  • Divide the workload between management and staff

Here, roles and responsibilities should clearly be defined.

Management should train workers and workers should implement lessons learned.

Examples of modern companies employing Scientific Management Theory

Although slightly outdated, scientific management theory is useful in highly competitive industries where labor costs need to be kept as low as possible.

Example organizations include:

  • Amazon Case Study

where warehouse staff are paid on a piece-rate basis according to their level of productivity.

The company has also recently introduced patented wristbands that track employee performance in real-time.

McDonald’s Case Study

The homogenization of McDonald’s restaurants worldwide has meant that processes have had to become extremely refined.

The procedure for everything from making a burger to mopping the floor is the same – regardless of geographic location.

These processes are ultra-efficient and are broken down into actionable steps, which is a core component of Taylorism.

The aviation industry case study

Scientific management theory has played a pivotal role in the evolution of airport and airline management – a competitive, time-sensitive, and heavily regulated industry that requires companies to manage a multitude of different tasks. 

Air New Zealand, for example, applied scientific management theory to its staff allocation and rostering systems over thirteen years between 1986 and 1999. Primarily, scientific management was used to address two core problems:

  • The tours-of-duty (ToD) planning problem – where a sequence of flights must be constructed to crew the flight schedule. These sequences can comprise one-day periods of work but also encompass longer sequences spanning consecutive days with multiple flights and rest periods, and
  • The rostering problem – where the airline has to match the ToD plan to individual employees to form a line of work (LoW) over a specific rostering period. In the process, airlines have to consider the employee’s skills or qualifications, employment contract conditions, operational rostering agreements, and any scheduled leave. 

The role of management and crew

In aviation, the interaction of these problems can be considered from both the point of view of management and crew. 

The management of Air New Zealand prefers maximum productivity and minimum-cost solutions that do not break laws and ensure all the work is performed.

They are also focused on the operational robustness of the schedule vis-à-vis sensitivity to disruptions.

For the Air New Zealand crew, on the other hand, the key concern is the quality of the solution.

What defines quality varies from one cohort to the next. Some consider the fair distribution of work to be important, while others hope to avoid arduous work patterns.

The importance of solving the aircrew-scheduling problem

Since aircraft and their associated crew are among the most expensive costs for an airline, their efficient utilization is vital to the company’s success and profitability. 

Lured by the potential to reduce costs, history is littered with airlines who tried and failed to develop effective optimization methods.

But it was not until the 1980s that computational power became sufficiently advanced to solve the ToD problem.

Development of the model 

In collaboration with the University of Auckland, Air New Zealand developed a total of 8 optimization-based systems. These systems, which were incorporated into the company’s database environment, solved all aspects of the planning and scheduling process across domestic and international routes.

One particular characteristic of these systems was that they presented solutions that exploited the rules. That is, the solutions were within the bounds of the law, made sense from a financial point of view, and were also beneficial for crew productivity and safety. 

Air New Zealand also collaborated with NASA in its pioneering research on measuring fatigue, with the results subsequently added to the ToD systems as additional rules and constraints.

In dollar terms, scientific management theory allowed the airline to reduce the amount of money it spent on hotels, meals, and other expenses for crew who traveled overseas. The cost of constructing and maintaining the crewing system has also decreased over time.

Despite the company’s airline fleet and route structure increasing in size and complexity, the number of people Air New Zealand needed to employ to solve scheduling problems dropped from 27 in 1987 to just 15 in 2000.

At the time, conservative estimates put the total cost saving of the initiative at 15.655 million NZD per annum .

Key takeaways

  • Scientific Management Theory is a theory of management that seeks to analyze and synthesize workflow to improve labor productivity.
  • Scientific Management Theory was originally based on the assumption that workers were only motivated by money and is heavily geared toward autocratic leadership styles. Nevertheless, it is still relevant to modern organizations.
  • Scientific Management Theory is particularly effective in industries with a high prevalence of menial or repetitive tasks where costs need to be minimized. Examples include Amazon and McDonald’s.

Key Highlights

  • Origin and Background: Scientific Management Theory was developed by Frederick Winslow Taylor in 1911. It aimed to improve industrial productivity through the application of engineering principles to the workplace. Taylor believed in finding the most efficient ways of performing tasks.
  • Worker Perceptions: In the early 20th century, there was a perception that workers were lazy and inefficient. Taylor’s theory aimed to address this by optimizing work processes.
  • Efficiency and Systematic Management: Taylor believed that inefficiency could be addressed through systematic management rather than relying on recruiting individuals with extraordinary work ethics. He emphasized the need for scientific analysis to identify the most efficient ways of performing tasks.
  • Taylorism: Taylor’s approach is often referred to as Taylorism. He believed in universal laws governing labor productivity and efficiency, and he introduced principles to optimize work processes.
  • Assumptions About Workers: Taylor’s classic assumptions included that workers found work unenjoyable, had a tendency to slack off (natural soldiering), and needed close monitoring and control. He believed in breaking down tasks into manageable actions and providing standardized training.
  • Piece-Rate Payment: Taylor advocated for paying workers based on their production, creating a win-win situation where employees earned more and businesses maximized profits.
  • Core Principles: Taylor’s principles include selecting methods based on science, matching workers to suitable roles, monitoring worker performance, and clearly defining roles and responsibilities between management and staff.
  • Modern Relevance: Although Taylorism is outdated in some aspects, its principles are still relevant, especially in industries where labor costs need to be minimized. Examples include Amazon and McDonald’s.
  • Amazon Case Study: Amazon uses piece-rate payment for warehouse staff based on productivity and employs real-time performance tracking technology.
  • McDonald’s Case Study: McDonald’s homogenized processes globally, ensuring consistency and efficiency in tasks like burger preparation and cleaning.
  • Aviation Industry Case Study (Air New Zealand): The aviation industry has applied Scientific Management Theory to crew scheduling and planning, achieving cost savings and efficiency improvements.
  • Air New Zealand’s Collaboration: Air New Zealand collaborated with the University of Auckland and NASA to develop optimization-based systems for crew scheduling, reducing costs and increasing efficiency.
  • Benefits of Scientific Management: The theory has been successful in optimizing processes, reducing costs, improving efficiency, and aligning worker capabilities with tasks.
  • Application and Limitations: Scientific Management Theory is effective in industries with repetitive tasks but may not fully accommodate the complexities of modern work environments.
  • Autocratic Leadership: Taylor’s approach is associated with autocratic leadership , which may not align with modern leadership trends emphasizing empowerment and collaboration.
  • Key Takeaways: Scientific Management Theory focuses on improving labor productivity through systematic analysis of work processes. It’s applicable in industries where repetitive tasks require optimization, and its principles are still relevant today.

What are the 4 Principles of Scientific Management?

The core principles of Scientific Management are:

What is the example of scientific management theory?

Cases of scientific management comprise companies like Amazon and McDonald’s, which have made defined business processes for inventory and fulfillment (Amazon) and fast food (McDonald’s) the core strengths of their organizations.

  • McDonald’s Case Study

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Scientific Management Theory Explained

Scientific Management Theory Concept

Last Updated June 28, 2022

What is Scientific Management Theory?

Scientific management theory is a method of improving efficiency in the workforce. As its name implies, this management theory uses scientific methods to assess work processes.

The scientific method consists of three steps: observation, experimentation, and analysis. In science, this could mean observing the effects of a treatment, experimenting with a different treatment, and analyzing the results. Similarly, managers use scientific management theory to observe their workplaces, test different methods of completing tasks, and analyze the effect of the changes.

When properly implemented, scientific management theory improves productivity. It is an evidence-based method that prioritizes efficiency and reliability. Having scientifically rigorous work methods in place creates clear expectations for employees because it establishes a single right way to do things. It also gives managers a unified standard against which to evaluate their employees.

Scientific management theory has grown exponentially since its inception. There are now a variety of management strategies that fall under the umbrella label of scientific management theory. Each of these strategies has its own set of strengths and weaknesses. It’s important to do your own research into scientific management theory to find the best applications for it in your workplace.

The History of Scientific Management Theory

The history of scientific management theory begins with 20th century mechanical engineer Frederick Winslow Taylor. In Taylor’s time, America was on the cusp of industrialization, but management methods had not yet changed to keep up with changes in technology. While working at a steel manufacturing plant, Taylor observed several production problems. 

For one thing, there was little specialization of labor or tools. Work shifts were randomly assigned, so inexperienced workers often ended up trying and failing to complete important projects. Tools were crude, and since only a small number of tools were used for every task, they wore out quickly. For another, there was no one single “best” standard for workers to aspire to. Everyone did their job in whatever way they thought worked best, regardless of whether it was effective. Finally, managers were completely disconnected from the workers they supervised. The average manager had no idea how the workers’ tasks were performed, so they were unable to provide suggestions for improvement.

Taylor set out to solve these problems. He designed specialized shovels and other tools. He advocated for workers to be matched to the projects for which they were most naturally gifted. He trained managers in his methods so that they could implement scientific management theory in their own workplaces.

Taylor is credited with revolutionizing productivity in the American workforce. At his own steel plant, the amount of pig iron the workers could transport in a day reportedly tripled once they adopted his methods. His ideas spread rapidly and helped give rise to the Industrial Age. Scientific management is sometimes even referred to as “Taylorism” in his honor.

Taylorism and Classical Management Theory

When people talk about “Taylorism,” they often mean scientific management theory as it existed in the early 20th century. This specific management style is also called classical management theory .

Classical management theory is distinguished by three characteristics: hierarchical structure, specialization, and financial incentives. In a company operating on classical management theory, there is a rigid hierarchy. Business owners are on top, supervisors are in the middle, and regular employees are on the bottom. Everyone has a specialized, small-scale task. Anyone who is especially successful is rewarded with financial benefits.

Classic Taylorism does a good job of addressing the physical needs of workers, but it ignores social needs and creativity. Inflexible hierarchies make it difficult for talented people to rise the ranks of leadership. Specialization is efficient, but it discourages people from experimenting, and therefore prevents the development of new methods. And although good pay incentivizes good behavior, money isn’t the only thing workers care about. Employees also want to feel valued and take pride in their work.

Classical management theory is no longer widely followed, but it still has uses. Since Taylor developed his theory while working in a manufacturing plant, classic Taylorism is well-designed for manufacturers. It also tends to function better in small enterprises where everyone knows each other, and social needs are easy to address.

The Principles of Scientific Management

There are four principles of Taylorism.

  • Choose methods based on science: Use the scientific method to determine the most efficient way to complete a task. Focus on increasing productivity and profits.
  • Assign workers to tasks based on their natural skillset:  Get to know your workers, discover what they’re good at, and place them where their skills will be the most useful.
  • Monitor your workers’ performance:  Observe what your workers are doing while they are on the clock so that you can quickly address any problems. If some workers are confused or unproductive, it is up to their managers to step in and fix the issue.
  • Divide workloads appropriately between workers and managers: Make sure that managers understand how to plan and train workers and that workers understand how to implement those plans.

Goals and Objectives of Scientific Management

The primary goal of scientific management is to increase efficiency. When Taylor began his scientific management experiments, he focused on increasing efficiency by reducing the amount of time needed to perform tasks. This was a good first step, but there’s a lot more to improving efficiency than just decreasing work time. Since Taylor’s time, other innovators have found more ways to increase efficiency, such as implementing automation software.

Another objective of scientific management theory is increasing profits. If everyone is working as efficiently as possible, then they should be able to produce huge amounts of high-quality products. That translates into more sales and bigger profit margins.

Real-World Applications of Scientific Management Theory

Scientific management theory is flexible enough to be applied in just about any industry. Whether you’re designing software or selling real estate, there are certain tasks that need to be done regularly. Identifying those tasks and optimizing them for efficiency is a great way to bring Taylorism into your workplace. Here’s an example.

Imagine your company has a newsletter mailing list. Every time a new person wants to be added to the mailing list, they send an email requesting to be added. An employee then manually adds them to the list.

This is an inefficient, multi-step method of adding newsletter subscribers. Your employee probably doesn’t get any job satisfaction from typing a name into a mailing list. Moreover, the time spent manually adding names is time that could be spent on more pressing projects.

If you were the manager tasked with implementing the principles of scientific management in this company, you might suggest designing a system that automatically adds people to the mailing list as soon as they submit a request. The subscribers get newsletter access sooner and the employee now has more time to concentrate on important assignments.

Applying Scientific Management Techniques

The theory of scientific management is not perfect. Optimizing efficiency while trying to maximize profits may not solve all your workplace problems. Moreover, Taylorism has been criticized as being ineffective for modern businesses. After all, Taylor was working in a pre-industrial era. He could not have foreseen how businesses and management styles would change in the future.

Taylor’s brand of scientific management may not be a perfect fit for contemporary life. However, the scientific management theory could be a starting point for designing your own management style. You also can consider other alternative management styles such as the Great Man Theory of Leadership and the Contingency Theory of Leadership .

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Frederick Taylor and Scientific Management

Understanding taylorism and early management theory.

By the Mind Tools Content Team

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How did current management theories develop?

People have been managing work for hundreds of years, and we can trace formal management ideas to the 1700s. But the most significant developments in management theory emerged in the 20th century. We owe much of our understanding of managerial practices to the many theorists of this period, who tried to understand how best to conduct business.

Historical Perspective

One of the earliest of these theorists was Frederick Winslow Taylor. He started the Scientific Management movement, and he and his associates were the first people to study the work process scientifically. They studied how work was performed, and they looked at how this affected worker productivity. Taylor's philosophy focused on the belief that making people work as hard as they could was not as efficient as optimizing the way the work was done.

In 1909, Taylor published " The Principles of Scientific Management ." [1] In this, he proposed that by optimizing and simplifying jobs, productivity would increase. He also advanced the idea that workers and managers needed to cooperate with one another. This was very different from the way work was typically done in businesses beforehand. A factory manager at that time had very little contact with the workers, and he left them on their own to produce the necessary product. There was no standardization, and a worker's main motivation was often continued employment, so there was no incentive to work as quickly or as efficiently as possible.

Taylor believed that all workers were motivated by money, so he promoted the idea of "a fair day's pay for a fair day's work." In other words, if a worker didn't achieve enough in a day, he didn't deserve to be paid as much as another worker who was highly productive.

With a background in mechanical engineering, Taylor was very interested in efficiency. While advancing his career at a U.S. steel manufacturer, he designed workplace experiments to determine optimal performance levels. In one, he experimented with shovel design until he had a design that would allow workers to shovel for several hours straight. With bricklayers, he experimented with the various motions required and developed an efficient way to lay bricks. And he applied the scientific method to study the optimal way to do any type of workplace task. As such, he found that by calculating the time needed for the various elements of a task, he could develop the "best" way to complete that task.

These "time and motion" studies also led Taylor to conclude that certain people could work more efficiently than others. These were the people whom managers should seek to hire where possible. Therefore, selecting the right people for the job was another important part of workplace efficiency. Taking what he learned from these workplace experiments, Taylor developed four principles of scientific management. These principles are also known simply as "Taylorism".

Four Principles of Scientific Management

Taylor's four principles are as follows:

  • Replace working by "rule of thumb," or simple habit and common sense, and instead use the scientific method to study work and determine the most efficient way to perform specific tasks.
  • Rather than simply assign workers to just any job, match workers to their jobs based on capability and motivation, and train them to work at maximum efficiency.
  • Monitor worker performance, and provide instructions and supervision to ensure that they're using the most efficient ways of working.
  • Allocate the work between managers and workers so that the managers spend their time planning and training, allowing the workers to perform their tasks efficiently.

Critiques of Taylorism

Taylor's Scientific Management Theory promotes the idea that there is "one right way" to do something. As such, it is at odds with current approaches such as MBO (Management By Objectives), Continuous Improvement initiatives, BPR (Business Process Reengineering), and other tools like them. These promote individual responsibility, and seek to push decision making through all levels of the organization.

The idea here is that workers are given as much autonomy as practically possible, so that they can use the most appropriate approaches for the situation at hand. (Reflect here on your own experience – are you happier and more motivated when you're following tightly controlled procedures, or when you're working using your own judgment?) What's more, front line workers need to show this sort of flexibility in a rapidly-changing environment. Rigid, rules-driven organizations really struggle to adapt in these situations.

Teamwork is another area where pure Taylorism is in opposition to current practice. Essentially, Taylorism breaks tasks down into tiny steps, and focuses on how each person can do his or her specific series of steps best. Modern methodologies prefer to examine work systems more holistically in order to evaluate efficiency and maximize productivity. The extreme specialization that Taylorism promotes is contrary to modern ideals of how to provide a motivating and satisfying workplace.

Where Taylorism separates manual from mental work, modern productivity enhancement practices seek to incorporate worker's ideas, experience and knowledge into best practice. Scientific management in its pure form focuses too much on the mechanics, and fails to value the people side of work, whereby motivation and workplace satisfaction are key elements in an efficient and productive organization.

The Principles of Taylor's Scientific Management Theory became widely practiced, and the resulting cooperation between workers and managers eventually developed into the teamwork we enjoy today. While Taylorism in a pure sense isn't practiced much today, scientific management did provide many significant contributions to the advancement of management practice. It introduced systematic selection and training procedures, it provided a way to study workplace efficiency, and it encouraged the idea of systematic organizational design.

To learn more about the current tools and practices of effective team management, visit our Team Management section.

[1] Taylor, Frederick Winslow (1911). ‘ The Principles of Scientific Management ,’ New York and London: Harper & Brothers.

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  • Published: 06 May 2021

Interpersonal relationships drive successful team science: an exemplary case-based study

  • Hannah B. Love   ORCID: orcid.org/0000-0003-0011-1328 1 ,
  • Jennifer E. Cross   ORCID: orcid.org/0000-0002-5582-4192 2 ,
  • Bailey Fosdick   ORCID: orcid.org/0000-0003-3736-2219 2 ,
  • Kevin R. Crooks 2 ,
  • Susan VandeWoude 2 &
  • Ellen R. Fisher 3  

Humanities and Social Sciences Communications volume  8 , Article number:  106 ( 2021 ) Cite this article

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  • Complex networks
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Scientists are increasingly charged with solving complex societal, health, and environmental problems. These systemic problems require teams of expert scientists to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes. Despite the essential need for such interdisciplinary interactions, little research has examined the impact of scientific team support measures like training, facilitation, team building, and expertise. The literature is clear that solving complex problems requires more than contributory expertise, expertise required to contribute to a field or discipline. It also requires interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group. These forms of expertise are often tacit and therefore difficult to access, and studies about how they are intertwined are nearly non-existent. Most of the published work in this area utilises archival data analysis, not individual team behaviour and assessment. This study addresses the call of numerous studies to use mixed-methods and social network analysis to investigate scientific team formation and success. This longitudinal case-based study evaluates the following question: How are scientific productivity, advice, and mentoring networks intertwined on a successful interdisciplinary scientific team? This study used applied social network surveys, participant observation, focus groups, interviews, and historical social network data to assess this specific team and assessed processes and practices to train new scientists over a 15-year period. Four major implications arose from our analysis: (1) interactional expertise and contributory expertise are intertwined in the process of scientific discovery; (2) team size and interdisciplinary knowledge effectively and efficiently train early career scientists; (3) integration of teaching/training, research/discovery, and extension/engagement enhances outcomes; and, (4) interdisciplinary scientific progress benefits significantly when interpersonal relationships among scientists from diverse disciplines are formed. This case-based study increases understanding of the development and processes of an exemplary team and provides valuable insights about interactions that enhance scientific expertise to train interdisciplinary scientists.

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Introduction

Scientists are increasingly charged with solving complex and large-scale societal, health, and environmental challenges (Read et al., 2016 ; Stokols et al., 2008 ). These systemic problems require interdisciplinary teams to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes (Barge and Shockley-Zalabak, 2008 ; De Montjoye et al., 2014 ; Fiore, 2008 ). Thus, to successfully approach complex research questions, scientific teams must synthesise knowledge from different disciplines, create a shared terminology, and engage members of a diverse research community (Matthews et al., 2019 ; Read et al., 2016 ). Despite significant time, energy, and money spent on collaboration and interdisciplinary projects, little research has examined the impact of scientific team support measures like training, facilitation, team building, and team performance metrics (Falk-Krzesinski et al., 2011 ; Klein et al., 2009 ).

Studies examining the development of scientific teaming skills that result in successful outcomes are sparse (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). The earliest studies of collaboration in science used bibliometric data to search for predictors of team success such as team diversity, size, geographical proximity, inter-university collaboration, and repeat collaborations (Borner et al., 2010 ; Cummings and Kiesler, 2008 ; Wuchty et al., 2007 ). Building from these studies, current research focuses on team processes. Literature suggests that to successfully frame a scientific problem, a team must also engage emotionally and interact effectively (Boix Mansilla et al., 2016 ) and that scientific collaboration involve consideration of the process, collaborator, human capital, and other factors that define an scientific collaboration (Bozeman et al., 2013 ; Hall et al., 2019 ; Lee and Bozeman, 2005 ). Similarly, Zhang et al. ( 2020 ) used social network analysis to examine how emotional intelligence is transmitted to team outcomes through team processes. Still more research is needed, and Hall et al. ( 2018 ) called for team science studies that use longitudinal designs and mixed-methods to examine project teams as they develop in order to move beyond bibliometric measures of success and to explore the complex, interacting features in real-world teams.

Fiore ( 2008 ) explained that much of what we know about the science of team science (SciTS), training scientists and team learning in productive team interactions, is anecdotal and not the result of systematic investigation (Fiore, 2008 ). Over a decade later there is still a paucity of research on how scientific teams develop the type of expertise they need to create new knowledge and further scientific discovery (Bammer et al., 2020 ). Bammer et al. ( 2020 ) has identified and defined two types of expertise: (1) contributory expertise, expertise required to make a contribution to a field or discipline (Collins and Evans, 2007 ); and (2) interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group (Bammer et al., 2020 ). These forms of expertise are often tacit, codified by “learning-by-doing,” and augmented from project to project; therefore, they are difficult to measure and rarely documented in literature (Bammer et al., 2020 ).

Wooten et al. ( 2014 ) outlined three types of evaluations—developmental, process, and outcome—needed to understand how teams develop and to provide information about their future success (Wooten et al., 2014 ). A developmental evaluation focuses on the continuous process of team development, and a process evaluation focuses on team interactions, meetings, and engagement (Patton, 2011 ). Both development and process evaluations have the common goal of understanding the team’s future success or failures, also known as the team’s outcomes (e.g., grants, publications, and awards) (Patton, 2011 ). The majority of published work on outcome metrics is evaluated by archival data analysis, not individual team behaviour and assessment (Hall et al., 2018 ). Albeit informative, these studies are based upon limited outcome metrics such as publications and represent only a selective sampling of teams that have achieved success. To collect these three types of evaluation data, it is recommended to engage mixed-methods research such as a combination of social network analysis (SNA), participant observation, surveys, and interviews, although these approaches have not been widely employed (Bennett, 2011 ; Borner et al., 2010 ; Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ).

A few key studies have provided insight into successful collaboration strategies. Duhigg ( 2016 ) found that successful teams provided psychological safety, had dependable team members, and relied upon clear roles and structures. In addition, successful teams had meaningful goals, and team members felt like they could make an impact through their work on the team (Duhigg, 2016 ). Similarly, Collins ( 2001 ) explained that in business teams, moving from “Good to Great” required more than selecting the right people; the team needed development and training to achieve their goals (Collins, 2001 ). Woolley et al. ( 2010 ) found that it is not collective intelligence that builds the most effective teams, but rather, how teams interact that predicts their success (Woolley et al., 2010 ). The three traits they identified as most associated with team success included even turn-taking, social sensitivity, and proportion female (when women’s representation nears parity with men) (Woolley et al., 2010 ). Finally, Bammer et al. ( 2020 ) recommended creating a knowledge bank to strengthen knowledge about contributary and interactional expertise in scientific literature to solve complex problems. Collectively, these studies argue that the key to collective intelligence is highly reliant on interpersonal relationships to drive team success.

This article reports on a longitudinal case-based study of an exemplary interdisciplinary scientific team that has been successful in typical scientific outputs, including competing for research awards, publishing academic articles, and training and developing scientists. This analysis examines how scientific productivity, advice, and mentoring networks intertwined to promote team success. The study highlights how the team’s processes to train scientists (e.g., developing mentoring and advice networks) have propelled their scientific productivity, fulfilled the University’s land grant mission (i.e., emphasises research/discovery; education/training; and outreach/engagement) and created contributory and interactional expertise on the team. Team dynamics were evaluated by social network surveys, participant observation, focus groups, interviews, and historical social network data over 15 years to develop theory and evaluate complex relationships contributing to team success (Dozier et al., 2014 ; Greenwood, 1993 ).

Case study selection

The [BLIND] Science of team science (SciTS) team consisted of scientists trained in four different disciplines and research administrators. The SciTS team monitored twenty-five interdisciplinary teams at [BLIND] for 5 years from initiation of team formation to identify team dynamics that related to team success. This case is thus presented as part of an ongoing study of the 25 teams, supported by efforts through the [BLINDED] to encourage and enhance collaborative, interdisciplinary research and scholarship. Team outcomes were recorded annually and included extramural awards, publications, presentations, students trained, and training outcomes. An exemplary case-based study is appropriate when the case is unusual, the issues are theoretically important, and there are practical implications (Yin, 2017 ). Further, cases can illustrate examples of expertise and provide guidance to future teams (Bammer et al., 2020 ). An “exemplary team designation” was given to this team by the SciTS evaluators. Metrics used to designate an exemplary team included: team outcomes; highly interdisciplinary research; longevity of the team; fulfilment of all aspects of the land grant mission (research/discovery; education/training; and outreach/engagement); integration of team members; and use of external reviewers.

Social network survey

The exemplary team included Principle Investigators (PIs), postdoctoral researchers (postdocs), graduate students, undergraduate students, and active collaborators external to the University. The entire team was surveyed annually 2015–2019 about the extent and type of collaboration with other team members. In 2015, the team was asked about prior collaborations, and in subsequent years they were asked about additional interactions since joining the team. Possible collaborative activities included research publications, scientific presentations, grant proposals, and serving on student committees. Team members were also asked the types of relationships they had with each team member, including learning, leadership, mentoring, advice, friendship, and having fun (Supplementary 2 ). Data were collected using a voluntary online survey tool (Organisational Network Analysis Surveys). All subjects were identified by name on the social network survey but are not identified in any network diagrams or analyses. SNA software programmes R Studio (R Studio Team, 2020 ) and UCINET (Borgatti et al., 2014 ) were used to analyse data and Visone (Brandes and Wagner, 2011 ) was used to create visualisations. The response rate for the survey was 94% in 2015, 83% in 2016, 95% in 2017, and 81% in 2018. All data collection methods were performed with the informed consent of the participants and followed Institutional Review Board protocol #19-8622H.

Data from the social network survey were combined to create three different network measures: scientific productivity, mentoring, and advice. The scientific productivity network was a combination of four survey measures: research/consulting, grants, publications, and serving on student committees. Scientific productivity represents a form of cognitive or contributory expertise: expertise required to contribute to a field or discipline (Bammer et al., 2020 ; Boix Mansilla et al., 2016 ). The mentoring and advice networks were created from social network survey questions: “who is your mentor?” and “who do you go to for advice?”, respectively. Mentor and advice are tacit forms of interactional expertise: socialised knowledge that includes socialisation into the practices of an expert group (Collins and Evans, 2007 ). Other studies have also found a connection between social characteristics of interdisciplinary work and other factors like productivity, career paths, and a group’s ability to exchange information, interact, and explore together (Boix Mansilla et al., 2016 ).

Social network data were summarised using average degree, sometimes split into indegree and outdegree. Outdegree is a measure of how many team members a given individual reported getting advice, or mentorship, from. Similarly, the indegree of an individual is a measure of how many other team members reported receiving advice, or mentorship, from that person. Average degree is the average number of immediate connections (i.e., indegree plus outdegree) for a person in a network (Giuffre, 2013 ; R. Hanneman and Riddle, 2005 a, 2005 b). To further explore the mentoring and advice networks, we calculated the average degree/outdegree/indegree of postdocs, graduate students, and faculty separately to directly compare demographic groups.

The advice, mentoring, and scientific productivity networks were directly compared using the Pearson correlation between the corresponding network adjacency matrices. We predicted a positive correlation between the advice, mentoring, and scientific productivity matrices. Statistical significance ( p  < 0.05) of correlations was assessed with the network permutation-based method Quadratic Assignment Procedure (QAP) (R. A. Hanneman and Riddle, 2005 a, 2005 b).

Historical social network data

A historical network survey was created to determine how the connections in the network formed, developed, and changed from project-to-project. The historical social network was constructed from three forms of data: interviews with the PIs, a historical narrative written by the PIs describing the team formation process, and team rosters that listed the 81 team members since the inception of the team.

Retrospective team survey

A retrospective team survey was administered at the end of the study to determine what skills team members developed and codified through participating on the team, how membership on the team supported members personally and professionally, and their favourite aspects of the team. The survey was sent to 22 members from the 2018 team roster using Qualtrics (Qualtrics Labs, 2005 ) with an 86% response rate.

Two semi-structured, one-hour interviews were conducted with two PIs in 2018 to learn about the history of the team. The interviews were digitally recorded and transcribed.

Participant observations

Participant observation was conducted from 2015–2019 at four annual three-day, off-campus retreats and 1–2 additional meetings each year. Students, PIs, external collaborators, and families were all invited to attend the retreats and meetings. Field notes about team interactions were recorded immediately after each interaction. The analytic field notes captured how team members interacted across disciplines, tackled scientific problems, and engaged with others at different career stages. Analysis occurred as field notes were written, during observations, and again during data analysis.

An exemplary team

The SciTS Team identified one team from the larger study and designated it as exemplary based on six (tacit and non-tacit) elements. First, the team had outstanding team outcomes. From 2004–2018, notable accomplishments include 33 extramural awards totalling over $5.6 million, including two large federal awards totalling over $4.5 million; 58 peer-reviewed publications with 39 different universities, 13 state agencies, and 11 other organisations; 141 presentations, 21 graduate students and 15 postdocs trained; and receipt of an [BLIND]institution-wide Interdisciplinary Scholarship Team Award. Participants received many individual honours, including one of the PIs being named to the National Academy of Sciences.

Second, this interdisciplinary team combined scientific expertise from many different backgrounds, including ecologists, wildlife biologists, evolutionary biologists, geneticists, veterinarians, and numerous collaborators. Principal Investigators were housed in five main universities: Colorado State University, University of Wyoming, University of Minnesota, University of California-Davis, and University of Tasmania. They also engaged collaborators from national and international universities, federal, state, and local governmental agencies, veterinary centres, and animal shelters. Collectively, team members represented 39 different universities, 11 federal agencies, 13 state agencies, and 11 other organisations listed on their peer-reviewed publications. The team has published globally with co-authors from every continent but Antarctica.

The third element identified was the team’s 15-year history and how they evolved project-to-project (Supplementary Video S1 ). In 2003, a graduate student proposed a collaborative research project between two faculty members who became two of the founding team PIs (Fig. 1 ). The team was formed in 2004 with four members—two faculty PIs, a postdoc, and a Ph.D. student (Fig. 1 ). Initial grant proposals submitted in 2005 and 2006 were not funded; however, in 2007, the team received a large federal research award from the US National Science Foundation (NSF). The team roster increased from four to nine, and a second large expansion occurred after receipt of another NSF award in 2012. By 2014, membership increased to 31 people, and at the end of analysis in 2018, the roster comprised 43 members. Over the course of observation, 81 different individuals, including students, faculty, and collaborators, had participated in research activities supported by the team.

figure 1

Significant events occurring over 15 years during the development and formation of an exemplary team.

The fourth reason this team was deemed exemplary was because it intertwined the components in the Land Grand mission, including research/discovery, teaching/training, and extension/engagement (Fig. 2 ). The team included undergraduates conducting research and presenting at conferences, graduate students working in multiple labs, and postdocs mentoring all the researchers in the lab. An external advisor said at the end of a retreat, “It’s really cool that students are part of the conversations that are both good/bad/ugly etc. It is not just good. It is not just one-on-one conversations. They hear it all.” A Ph.D. student wrote in the Retrospective Survey about the skills he developed: “I have developed the ability to talk about my research to people outside my field. I have also worked on broadening my understanding of disease ecology as a whole. I have been given the opportunity [to] begin placing my work in the larger framework of ecosystem health.” Faculty also wrote about what they learned, “[I] Learned from leadership of team (especially [blinded], and other PIs) how to develop and conduct research team work well - am using what I am learning to develop new research teams…. how to develop and nurture and respect interpersonal relationships and diversity of opinions. This has been an amazing experience, to be part of a well-functioning team, and to examine why and how that is maintained”

figure 2

The team grew from 4 members in 2004 to 42 members in 2018. Much of the growth occurred by the addition of students and external collaborators.

Fifth, the team was effective at onboarding and integrating new members. To do so, they used two key strategies (Fig. 3 ). First, 15 of the students held co-advised graduate research positions. This shared model of mentorship provided students with opportunities to work in multiple labs, collaborate with additional team members, and gain a broader academic experience. A Ph.D. student wrote in the Retrospective Survey about the skills she learned from being a member of the team: “Leadership skills, communicating science to those in other fields, scientific writing skills, technical laboratory skills, interpersonal communication skills, data sharing experience, and many others.” The shared model supported the team’s interdisciplinary mission by providing opportunities to train future scientists to communicate, network, and conduct research across disciplines. Second, as team members developed through participation on the team, they assumed more mature scientific roles. Fourteen members of the team changed positions within the team. Many of these transitions were from undergraduate student to Ph.D. student or Ph.D. student to postdoctoral researcher. In 2012, one postdoc became a PI on the grant.

figure 3

Social network diagrams of team growth and development from 2004–2018. This network reports onboarding and integration of all members, including their primary position when they joined the team. The nodes are sized by average degree (see text). Colours denote different roles on the team.

Finally, the 2018 team retreat included external reviewers. At the end of 2018 team retreat, they were asked if they had any feedback for the team. An external reviewer said: “You can check all of the boxes of a good team.”; “This is a dream team.”; “I am really impressed.”. Another external reviewer said:

The ambitiousness to execute the scope of the project, to have this many PIs, to be able to communicate; the opportunities for new insights; and the opportunities it presents for trainees are rare. There are a lot of people exposed in this. This is a unique experience for someone in training. And it extends to elementary school. I don’t think there are many projects that have this type of scope. I was impressed with just the idea that scientists are taking this across such a great scope and taking on such great questions.

Scientific productivity network

Prior to 2016, the average degree of the scientific productivity network was 8.8 (Fig. 4 ). In 2016, four faculty nodes were in the core of the network, and the periphery nodes included graduate students, postdocs, and external collaborators (Fig. 5 ). The average degree dropped slightly to 6.2 when the team integrated new members and re-formed around new roles and responsibilities on a new grant (Fig. 4 ). In 2017, the average degree peaked at 9.7 (Fig. 4 ) and faculty were still core, but graduate students and postdocs were more central than before (Fig. 5 ). During this time, productivity was at its highest as team members were working together to meet the objectives of a 5-year interdisciplinary NSF award. The network evolved further in 2018; two of the postdoc nodes overlapped with the faculty nodes in the core of the network (Fig. 5 ).

figure 4

Average degree of social networks diagrams (mentoring, advice, scientific productivity) indicated strong social ties among team members.

figure 5

Social network measures of productivity (research/consulting, grants, publications, and serving on student committees) were recorded over time. Each node represents a person on the team, and nodes are sized by average degree (see text). Colours denote different roles on the team. The node label indicates the number of years a person has been part of the team.

Mentoring is integral in the collaborative network

Team members reported between an average of 2.4–3.1 mentors (average outdegree) each year on the team (Fig. 6 ). More specifically, graduate students reported 6.0–7.7 mentors, whereas postdocs reported 2.4–3.5 mentors (Table 1 ). Faculty team members reported having an average of 2.2 to 4.3 mentors on the team (Table 1 ), with the highest average outdegree in 2018.

figure 6

This diagram was created by using participant answers to the social network question, “who is your mentor?” Each circle or node represents a person on the team. The nodes are sized by outdegree to show who reported receiving mentorship. Node size indicates how many mentors an individual reported, and arrows indicate nodes that served as mentors. Colours denote different roles on the team.

The highest indegree for an individual was the lead PI, with an indegree ranging from 13 to 14 each year (i.e., each year, 13–14 team members reported this individual provided mentorship). In response to an interview question about this PIs favourite part of the team, this individual said, “…and of course, I really like the mentorship of the students…They are initially naive, and some people are initially underconfident, but eventually they become fluent in their subject area.” Many students wrote about the mentoring they received from the team. An undergraduate student wrote:

I have improved my communication skills after needing to collaborate with several mentors across different time zones. I’ve also improved willingness to ask questions when I don’t understand a concept. I’ve also learned what concepts I find basic in my field that others outside my discipline are less familiar with.

Faculty also wrote about the mentoring they received, such as, “I continually learn from members in the team and mentorship by the more experienced members has supported my own career progression.”

Advice is integral in the collaborative network

In the 2015–2017 advice network diagrams, the faculty were tightly clustered (Fig. 7 ). In 2018, the cluster separated as postdocs and graduate students joined the centre of the network. On average, team members reported 5.1 to 6.4 people they could go to for advice (Fig. 4 ).

figure 7

This diagram was created by using participant answers to the social network question, “who do you go to for advice?” Each circle or node represents a person on the team. The nodes are sized by outdegree to show who reported receiving mentorship. Node size indicates how many mentors an individual reported, and arrows indicate nodes that served as mentors. Colours denote different roles on the team.

In a survey, faculty responded to the question, “How has the team supported you personally and professionally?” One faculty member wrote: “Just today I asked three members of the team for professional advice! And got a thoughtful and prompt response from all.” Another team member wrote: “Being a member of the…team has allowed me to develop skills in statistical analysis, scientific writing, and critical thinking. This team has opened my eyes to what is possible to achieve with science and has provided me with opportunities to network and expand my horizons both within the field of study and outside of it.” These quotes further suggest that the mentoring and advice from a large interdisciplinary team were important to train future scientists.

Interpersonal relationships as driver for scientific productivity

The mentoring and advice networks supported and built on the scientific productivity network and vice versa. The correlation between the collaboration, mentoring, and advice networks would not be possible if the networks were not intertwined. In the retrospective survey, a faculty member described how tacit interpersonal relationships were correlated with their scientific productivity:

Being a part of this grant has helped me both personally and professionally by teaching me new skills (disease ecology, team dynamics), developing friendships/mentors from the team, and strengthening my CV and dossier for promotion to early full professorship.

A Ph.D. student also described how the relationships on the large team propelled their research.

Membership on this team has provided me with a lot of mentorship that I would not otherwise receive were I not working on a large multi-disciplinary for my doctoral research. It has also allowed me to network more effectively.

Between 2015 and 2018, the mentor and advice networks were significantly correlated with the scientific productivity network, demonstrating that personal relationships are associated with scientific collaboration (Table 2 ).

To date, the literature examining successful interdisciplinary scientific team skills that result in successful outcomes is sparse (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). The majority of published work in this area is evaluated by archival data analysis, not individual team behaviour and assessment (Hall et al., 2018 ). This study answers the call of numerous researchers to use mixed-methods and SNA to investigate scientific teams (Bennett, 2011 ; Borner et al., 2010 ; Hall et al., 2018 ; Woolley et al., 2010 ; Wooten et al., 2015 ). Our case-based study also increases understanding of the development and processes of an exemplary team by providing valuable insights about how the interactions that enhance scientific productivity are synergistic with the interactions that train future scientists. There are four major implications of our findings: (1) interactional and contributory expertise are intertwined; (2) team size, tacit knowledge gained from previous project, and interdisciplinary knowledge were used to effectively and efficiently train scientists; (3) the team increased scientific productivity through interpersonal relationships; and (4) the team fulfilled the land grant mission of the University by integrating teaching/training, research/discovery, and extension/engagement into the team’s activities.

Interactive and contributory expertise are intertwined

Previous literature on scientific teams has found that great teams are not built on scientific expertise alone, but on the processes and interactions that build psychological safety, create a shared language, engage members emotionally, and promote effective interactions (Boix Mansilla et al., 2016 ; Hall et al., 2019 ; Senge, 1991 ; Woolley et al., 2010 ; Zhang et al., 2020 ). The team highlighted in this report created a shared language and vision through the mentoring and advice networks that helped fuel the team’s scientific productivity (Hall et al., 2012 ). To solve complex problems requires more than contributory expertise, it also requires interactional expertise (Bammer et al., 2020 ). These forms of expertise are often tacit and internalised through the process of becoming an expert in a field of study (Collins and Evans, 2007 ). Learning-by-doing is augmented from project-to-project, with expertise codified over time (Bammer et al., 2020 ). Further, cognitive, emotional, and interactions are key components of successful collaborations (Boix Mansilla et al., 2016 ; Bozeman et al., 2013 ; Zhang et al., 2020 ). Using social network analysis, our case-based analysis found that the mentoring and advice ties were intertwined with the scientific productivity network.

Training scientists to be experts

The Retrospective Survey asked what personal and professional skills respondents learned from being a member of a team. We hypothesised that many respondents would report tangible skills. Surprisingly, 82% of the open-ended responses were about tacit skills. Students frequently had co-advised graduate research positions, worked in multiple labs, and communicated regularly with practitioners. Moreover, the team translated research to different disciplines within the team, mentored others, and managed interpersonal conflicts. These interactions built expertise because training was not limited to research in a single lab or only in an academic setting. Simple, discrete, and codified knowledge is relatively easy to transfer; however, teams need stronger relationships to gain complex and tacit knowledge, (Attewell, 1992 ; Simonin, 1999 ). On this team, interactions and the ability to practice communication were especially influential for students, junior scientists, and new members. These individuals provided survey responses reporting they learned a wide variety of skills ranging from leadership, scientific and interpersonal communication, networking across disciplines, scientific writing, laboratory techniques, and data sharing standards. Further, respondents noted they had gained experience in developing, nurturing, and respecting interpersonal relationships and diversity of opinions. This was reinforced with participant observation data. In other interdisciplinary groups studied in conjunction with this exemplary team, students were not typically exposed to the inner workings of the team such as leadership meetings. On this team, students were exposed to all conversations, which became an important component of the mentoring and advice structure, serving to train future scientists in all aspects of team integration and leadership development. Belonging to this large interdisciplinary team was effectively training, building, and structuring the team.

Interpersonal relationships increase scientific productivity

Longevity of relationships is an important factor in creating social cohesion, reducing uncertainty, and increasing reliability and reciprocity (Baum et al., 2007 ; Gulati and Gargiulo, 1999 ; Phelps et al., 2012 ). Previous literature has, however, rarely documented the importance of time in building the structure of the network (Phelps et al., 2012 ) and few longitudinal studies of scientific teams exist. Further, it has long been hypothesised that greater interaction among people increases the quality and innovativeness of ideas generated, which may in turn increase productivity (Cimenler et al., 2016 ). Our case-based study found that the mentoring and advice ties existed in a symbiotic relationship with the scientific productivity network where the practices of the team were simultaneously training scientists. This aligns with social network literature that interactions can structure the social network and the network structure influences interactions (Henry, 2009 ; Phelps et al., 2012 ). Second, intentional mentoring programmes have demonstrated a positive relationship between interdisciplinary mentoring and increased research productivity outcomes such as grant funding and publications (Spence et al., 2018 ). Finally, this finding also aligns with literature on the generation of new knowledge (Phelps et al., 2012 ). Knowledge creation has traditionally been framed in terms of individual creativity, but recent studies have placed more emphasis on how the contribution of social dynamics are influential in explaining this process (Boix Mansilla et al., 2016 ; Csikszentmihalyi, 1998 ; Phelps et al., 2012 ; Sawyer, 2003 ; Zhang et al., 2009 ). Thus, while we might think that science drives the team, in this case-based study, the team’s interpersonal relationships were the driver of the team’s scientific productivity.

Fulfilling the land grant mission

As noted above, this exemplary team fulfilled all three goals of the land grant mission. First, the team was training scientists at all levels, from undergraduate students, to graduate students, postdocs, new faculty, and external collaborators, including community partners. In many instances, the training and mentoring was structured in a vertically integrated manner. For example, postdocs were training graduate and undergraduate students, typical of many teams. In addition to the “top-down” scenarios, however, the team also encouraged training that went from the bottom up as well. Effectively, this is a hallmark of successful teams in other sectors such as emergency responders and elite military teams – whomever has the knowledge to drive the issue at hand is the effective “leader” in that mission (Kotler and Wheal, 2008 ). Second, the team excelled in research and discovery, partnering with a diversity of external collaborators to do so. This created a network structure wherein the team clearly utilised the collaborators for mentoring and advice. Organisations with a core-periphery network structure like this team have been reported to be more creative because ties on the periphery, such as external collaborators, can span boundaries and access diverse information (Perry-Smith, 2006 ; Phelps et al., 2012 ). Finally, because the team’s collaborators included community partners and practitioners, they were also influencing policy and practice. This resulted in an overall greater impact for the team’s science and allowed them to tailor their research to best meet the needs of society (Barge and Shockley-Zalabak, 2008 ).

Future research

This study provides a unique contribution to team science literature because it longitudinally studied the development and processes of a successful interdisciplinary team (Wooten et al., 2014 ). Future research on the elements of effective interdisciplinary teaming is required in five key areas. First, identification of best practices that inhibit or support teams is necessary (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). Second, previous research has found that small teams are best at disrupting science with new ideas and opportunities (Wu et al., 2019 ); however, practices large teams use to create new knowledge have been poorly documented. Third, successful training concepts for graduate students and postdoctoral researchers need additional consideration (Knowlton et al., 2014 ; Ryan et al., 2012 ; Sarraj et al., 2017 ). Fourth, we hypothesise that graduate students act as bridges in teams to connect scientific disciplines and prevent clustering the network. Future research should investigate the role of graduate students in creating knowledge through interdisciplinary teams. Finally, additional research is needed to better recognise and reward scientists who undertake integration and implementation (Bammer et al., 2020 ).

Data availability

The datasets generated during and analysed during the current study are available in the Mountain Scholar repository, https://doi.org/10.25675/10217/214187

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Acknowledgements

A special thank you to Elizabeth Scodfidio for helping with data, images and more!. The research reported in this publication was supported by Colorado State University’s Office of the Vice President for Research Catalyst for Innovative Partnerships Programme. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of the Vice President for Research. Supported by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. Contents are the authors’ sole responsibility and do not necessarily represent official NIH views. Funding and support were provided by grants from the National Science Foundation’s Ecology of Infectious Diseases Programme (NSF EF-0723676 and NSF EF-1413925).

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Love, H.B., Cross, J.E., Fosdick, B. et al. Interpersonal relationships drive successful team science: an exemplary case-based study. Humanit Soc Sci Commun 8 , 106 (2021). https://doi.org/10.1057/s41599-021-00789-8

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scientific management case study examples

Module 2: History of Management

Scientific management, learning outcomes.

  • Explain the concept of scientific management.
  • Summarize the work of Frederick W. Taylor.
  • Summarize the work of Frank and Lillian Gilbreth.
  • Summarize the work of Henry Gantt.

Prior to the early 1900s, there was no management theory as we think of it today. Work happened as it always had—those with the skills did the work in the way they thought best (usually the way it had always been done). The concept that work could be studied and the work process improved did not formally exist before the ideas of Frederick Winslow Taylor.

The scientific management movement produced revolutionary ideas for the time—ideas such as employee training and implementing standardized best practices to improve productivity. Taylor’s theory was called scientific because to develop it, he employed techniques borrowed from botanists and chemists, such as analysis, observation, synthesis, rationality, and logic. You may decide as you read more about Taylor that by today’s criteria he was not the worker’s “friend.” However, Taylor must be given credit for creating the concept of an organization being run “as a business” or in a “businesslike manner,” meaning efficiently and productively.

Practice Question

Frederick w. taylor.

A headshot of Frederick Winslow Taylor

Frederick Taylor (1856–1915) is called the Father of Scientific Management.

Before the Industrial Revolution, most businesses were small operations, averaging three or four people. Owners frequently labored next to employees, knew what they were capable of, and closely directed their work. The dynamics of the workplace changed dramatically in the United States with the Industrial Revolution. Factory owners and managers did not possess close relationships with their employees. The workers “on the floor” controlled the work process and generally worked only hard enough to make sure they would not be fired. There was little or no incentive to work harder than the next man (or woman).

Taylor was a mechanical engineer who was primarily interested in the type of work done in factories and mechanical shops. He observed that the owners and managers of the factories knew little about what actually took place in the workshops. Taylor believed that the system could be improved, and he looked around for an incentive. He settled on money. He believed a worker should get “a fair day’s pay for a fair day’s work”—no more, no less. If the worker couldn’t work to the target, then the person shouldn’t be working at all. Taylor also believed that management and labor should cooperate and work together to meet goals. He was the first to suggest that the primary functions of managers should be planning and training.

In 1909, Taylor published The Principles of Scientific Management . In this book, he suggested that productivity would increase if jobs were optimized and simplified. He also proposed matching a worker to a particular job that suited the person’s skill level and then training the worker to do that job in a specific way. Taylor first developed the idea of breaking down each job into component parts and timing each part to determine the most efficient method of working. Soon afterward, two management theorists, Frank and Lillian Gilbreth, came up with the idea of filming workers to analyze their motions. Their ideas have since been combined into one process (called time and motion studies) for analyzing the most productive way to complete a task.

Scientific management has at its heart four core principles that also apply to organizations today. They include the following:

  • Look at each job or task scientifically to determine the “one best way” to perform the job. This is a change from the previous “rule of thumb” method where workers devised their own ways to do the job.
  • Hire the right workers for each job, and train them to work at maximum efficiency.
  • Monitor worker performance, and provide instruction and training when needed.
  • Divide the work between management and labor so that management can plan and train, and workers can execute the task efficiently.

Taylor designed his approach for use in places where the work could be quantified, systemized, and standardized, such as in factories. In scientific management, there is one right way to do a task; workers were not encouraged (in fact, they were forbidden) to make decisions or evaluate actions that might produce a better result. Taylor was concerned about the output more than worker satisfaction or motivation. Taylor’s work introduced for the first time the idea of systematic training and selection, and it encouraged business owners to work with employees to increase productivity and efficiency. And he introduced a “first-class worker” concept to set the standard for what a worker should be able to produce in a set period of time. Scientific management grew in popularity among big businesses because productivity rose, proving that it worked.

Today, an updated version of his original theory is used by such companies as FedEx and Amazon. Digital Taylorism  is based on maximizing efficiency by standardizing the tools and techniques for completing each task involved with a given job. Every task is broken down to the smallest motion and translated into an exact procedure that must be followed to complete that task. Because everyone is operating in the same mechanistic way, it increases predictability and consistency while reducing errors. It is relatively easy for managers to replace workers and retain the same productivity. The criticism of this type of management approach is similar to that of Taylor’s original theory: It reduces worker creativity; it requires management to monitor all aspects of employee behavior; and it is unforgiving to workers who don’t meet the standard.

Frank and Lillian Gilbreth

Two more pioneers in the field of management theory were Frank and Lillian Gilbreth, who conducted research about the same time as Taylor. Like Taylor, the Gilbreths were interested in worker productivity, specifically how movement and motion affected efficiency.

Portrait of Lillian Gilbreth

Lillian Gilbreth. The book and film Cheaper By the Dozen were based on her and Frank’s experiences raising twelve children according to their theories of time and motion studies.

As stated above, the Gilbreths used films to analyze worker activity. They would break the tasks into discrete elements and movements and record the time it took to complete one element. In this way, they were able to predict the most efficient workflow for a particular job. The films the Gilbreths made were also useful for creating training videos to instruct employees in how to work productively.

Taylor and the Gilbreths belonged to the classical school of management , which emphasized increasing worker productivity by scientific analysis. They differed, however, on the importance of the worker. Taylor’s emphasis was on profitability and productivity; the Gilbreths were also focused on worker welfare and motivation. They believed that by reducing the amount of motions associated with a particular task, they could also increase the worker’s well-being. Their research, along with Taylor’s, provided many important principles later incorporated into quality assurance and quality control programs begun in the 1920s and 1930s. Eventually, their work led to the science of ergonomics and industrial psychology. ( Ergonomics is the study of people in their operating environment, with the goal of increasing productivity and reducing risk of work-related injury.)

You can watch some of the Gilbreths’ films below to get an idea of how they documented their time and motion studies in an effort to increase efficiency and safety.

Henry Gantt

Henry Gantt (1861–1919) was also an associate of Taylor. He is probably best known for two key contributions to classical management theory: the Gantt chart and the task and bonus system.

The Gantt chart is a tool that provides a visual (graphic) representation of what occurs over the course of a project. The focus of the chart is the sequential performance of tasks that make up a project. It identifies key tasks, assigns an estimated time to complete the task, and determines a starting date for each element of a task. Gantt differentiated between a terminal element that must be completed as part of a larger task. The related terminal elements together created what he called the summary element .

An example of a simple Gantt chart

The Gantt chart has multiple benefits for project management:

  • It aids in the breakdown of tasks into specific elements.
  • It allows for the monitoring of projected timelines.
  • It identifies which tasks are dependent upon a prior task or element and which are independent and can be completed at any time.

Let’s apply the Gantt chart principles to a simple project. Imagine that you want to paint a room. The summary element is the finished, painted room. The individual terminal tasks might include calculating the square footage of the room, preparing the walls, choosing the paint, purchasing the paint, putting down the drop cloth, taping the windows, applying the paint, and final cleanup. Some of these elements are independent, and some elements are dependent upon others. Purchasing the paint is dependent upon knowing the square footage and choosing the paint color. Before painting can start, the walls must be prepared and the paint must be purchased. But purchasing the paint is not dependent upon preparing the walls—these tasks could be started at the same time.

A white-walled room with two step stools, clear plastic, and several cans of paint on the floor

There are several distinct tasks involved in painting a room.

Gantt also promoted the task and bonus plan that modified Taylor’s “a fair day’s pay for a fair day’s work” premise. Gantt wanted to establish a standard (average) time for a piece of work or task. Then, if a worker took more than the standard time, his pay was docked. But if he took less time, he was paid for the additional pieces of work and a bonus of up to 20 percent more. Also known as the progressive rate system , this plan was preferred by workers who were willing to work harder for additional wages.

Although Gantt is not the best known of the classic management theorists, many of his ideas are still being used in project management.

Scientific management was the first widespread promotion of rational processes to improve efficiency. The goal was to develop a standard against which work performance could be measured. Training became an important part of the management process. By the 1930s, however, many unions and workers were suspicious of the intentions of scientific management.

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Case Study on Scientific Management

Scientific management case study:.

Scientific management is the process of improvement of the quality of work on the basis of the scientific progress and the latest experience in this sphere.

With the help of the scientific management generally the work of a separate business, plant, factory or the organization is improved, because it is impossible to analyze the general norms which can suit the organization of work at every organization. Nevertheless, the process of scientific management in the conditions of socialism is considered to be useful for the organization of work of the whole society. Frederic Taylor is the founder of the theory of scientific management and he introduced all the principles and the meaning of the process for the general public.The process of scientific management has always been interesting for the businessman who wanted to improve their business. With the help of scientific management it is possible to organize the work of the company wisely introducing the latest modern machinery which can raise the quality and the quantity of the fulfilled work. Then, one can calculate the number of the required employees in his company which will maintain the quality of the working process.

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Next, with the help of scientific management it is possible to improve the quality of the workplaces; improving the work of the staff as a result. Finally, scientific management is useful to create the optimal training and development courses for the novice employees who expect to get a job at a company and become skillful and professional workers in future.Scientific management is an important and useful process which can optimize the work of the whole company wisely. Naturally, every businessman wants his company to function perfectly well, so scientific management is the best way to value the potential of the company and the staff and make the basis for the highest profits. A good scientific management case study should be informative, interesting and answer to all the questions of the suggested problem. One should analyze the case site, research the problem which occurred there and understand the factors, which influenced the problem.

A student should collect much data to be able to analyze the cause of the problem and then value its effect. Generally, every case study is the puzzle which requires solution, so a student is expected to brainstorm effective methods which can solve the problem professionally.The assignment is quite difficult for the inexperienced students, so most of them look for the way out in the Internet. A free sample case study on scientific management in the web is quite a useful piece of advice for every student. If a student has troubles with composition and formatting, a free example case study on scientific management theory is a reasonable decision.

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  • What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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Hertz CEO Kathryn Marinello with CFO Jamere Jackson and other members of the executive team in 2017

Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

  • Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
  • Fifty four percent of raw case users came from outside the U.S..
  • The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
  • Twenty-six of the cases in the list are raw cases.
  • A third of the cases feature a woman protagonist.
  • Orders for Yale SOM case studies increased by almost 50% compared to 2020.
  • The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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Scientific Management Case Studies Samples For Students

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Case Study On Principles Of Theory And How Manager Should Do The Job

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Introduction

Case study on call centre, introduction:.

Fleming and Sturdy (2011) describe the managerial regime in a call centre. Please read their case description closely.

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Good political science case study example.

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Example Of Case Study On Role Of The Manager And The Impact Of Organizational Theories On Managers

Management is a task that brings together the various aspects of the roles of managers. In particular, the tasks performed by managers in organizations are quite huge because it is essential that a person is courageous enough to deal with the various views and characteristics of people that make up the workforce of the organization (Rice, 2013). Therefore, the analysis of the effects of the management theories on the management roles of managers is a timely analysis that requires adequate time and resources for the investigation of the various aspects of the subject.

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Defining case management success: a qualitative study of case manager perspectives from a large-scale health and social needs support program

Margae knox.

1 School of Public Health, University of California, Berkeley, Berkeley, California, USA

Emily E Esteban

2 Contra Costa Health Services, Martinez, California, USA

Elizabeth A Hernandez

Mark d fleming, nadia safaeinilli, amanda l brewster, associated data.

No data are available. Data are not publicly available to protect potentially sensitive information. For data inquiries, please contact the corresponding author.

Health systems are expanding efforts to address health and social risks, although the heterogeneity of early evidence indicates need for more nuanced exploration of how such programs work and how to holistically assess program success. This qualitative study aims to identify characteristics of success in a large-scale, health and social needs case management program from the perspective of interdisciplinary case managers.

Case management program for high-risk, complex patients run by an integrated, county-based public health system.

Participants

30 out of 70 case managers, purposively sampled to represent their interdisciplinary health and social work backgrounds. Interviews took place in March–November 2019.

Primary and secondary outcome measures

The analysis intended to identify characteristics of success working with patients.

Case managers described three characteristics of success working with patients: (1) establishing trust; (2) observing change in patients’ mindset or initiative and (3) promoting stability and independence. Cross-cutting these characteristics, case managers emphasised the importance of patients defining their own success, often demonstrated through individualised, incremental progress. Thus, moments of success commonly contrasted with external perceptions and operational or productivity metrics.

Conclusions

Themes emphasise the importance of compassion for complexity in patients’ lives, and success as a step-by-step process that is built over longitudinal relationships.

What is already known on this topic?

  • Case management programs to support health and social needs have demonstrated promising yet mixed results. Underlying mechanisms and shared definitions of successful case management are underdeveloped.

What this study adds?

  • Case managers emphasised building trust over time and individual, patient-defined objectives as key markers of success, a contrast to commonly used quantitative evaluation metrics.

How this study might affect research, practice or policy?

  • Results suggest that lighter touch case management interventions face limitations without an established patient relationship. Results also support a need for alternative definitions of case management success including patient-centered measures such as trust in one’s case manager.

Introduction

Health system efforts to address both health and social needs are expanding. In the USA, some state Medicaid programmes are testing payments for non-medical services to address transportation, housing instability and food insecurity. Medicaid provides healthcare coverage for lower income individuals and families, jointly funded by federal and state governments. Similarly, social prescribing, or the linking of patients with social needs to community resources, is supported by the UK’s National Health Service and has also been piloted by Canada’s Alliance for Healthier Communities. 1

A growing evidence base suggests promising outcomes from healthcare interventions addressing social needs. In some contexts, case managers or navigators providing social needs assistance can improve health 2 and reduce costly hospital use. 3–5 Yet systematic reviews also report mixed results for measures of health and well-being, hospitalisation and emergency department use, and overall healthcare costs. 6–9 Notably, a randomised trial of the Camden Care Coalition programme for patients with frequent hospitalisations due to medically and socially complex needs 10 found no difference in 180-day readmission between patients assigned to a care transitions programme compared with usual hospital postdischarge care. In the care transition programme, patients received follow-up from a multidisciplinary team of nurses, social workers and community health workers. The team conducted home visits, scheduled and accompanied patients to follow-up outpatient visits, helped with managing medications, coached patients on self-care and connected patients with social services and behavioural healthcare. The usual care group received usual postdischarge care with limited follow-up. 11 This heterogeneity of early evidence indicates a need for more nuanced explorations of how social needs assistance programmes work, and how to holistically assess whether programmes are successful. 12 13

Social needs case management may lead to health and well-being improvements through multiple pathways involving both material and social support. 14 15 Improvements are often a long-term, non-linear process. 16 17 At the same time, quality measures specific to social needs assistance programmes currently remain largely undefined. Studies often analyse utilisation and cost outcomes but lack granularity on interim processes and markers of success.

In order to translate a complex and context-dependent intervention like social needs case management from one setting to another, these interim processes and outcomes need greater recognition. 18–20 Early efforts to refine complex care measures are underway and call out a need for person-centred and goal-concordant measures. 21 Further research on how frontline social needs case managers themselves define successes in their work could help leaders improve programme design and management and could also inform broader quality measure development efforts.

Our in-depth, qualitative study sought to understand how case managers defined success in their work with high-risk patients. Case managers were employed by CommunityConnect, a large-scale health and social needs care management programme that serves a mixed-age adult population with varying physical health, mental health and social needs. Each case manager’s workflow includes an individualised, regularly updated dashboard of operational metrics. It is unclear, however, whether or how these operational factors relate to patient success in a complex care programme. Thus, the case managers’ perspectives on defining success are critical for capturing how programmes work and identifying essential principles.

Study design and setting

In 2017, the Contra Costa County Health Services Department in California launched CommunityConnect, a case management programme to coordinate health, behavioural health and social services for County Medicaid patients with complex health and social conditions. The County Health Services Department serves approximately 15% (180 000) of Contra Costa’s nearly 1.2 million residents. CommunityConnect enrollees were selected based on a predictive model, which leveraged data from multiple county systems to identify individuals most likely to use hospital or emergency room services for preventable reasons. Enrollees are predominantly women (59%) and under age 40 (49%). Seventy-seven per cent of enrollees have more than one chronic condition, particularly hypertension (42%), mood disorders (40%) and chronic pain (35%). 22 Programme goals include improving beneficiary health and well-being through more efficient and effective use of resources.

Each case manager interviewed in this study worked full time with approximately 90 patients at a time. Case managers met patients in-person, ideally at least once a month for 1 year, although patients sometimes continue to receive ongoing support at the case manager’s discretion in cases of continued need. Overall, up to 6000 individuals at a time receive in-person case management services through CommunityConnect, with approximately 200–300 added and 200–300 graduated per month. At the time of the study, CommunityConnect employed approximately 70 case managers trained in various public health and social work disciplines (see table 1 , Interview Sample). Case managers and patients are matched based on an algorithm that prioritises mental health history, primary language and county region.

Interview sample

Although case managers bring unique experience from their respective discipline, all are expected to conduct similar case management services. Services included discussing any unmet social needs with patients, coordinating applicable resources and partnering with the patient and patient’s care team to improve physical and emotional health. The programme tracks hospital and emergency department utilisation as well as patient benefits such as food stamps, housing or transportation vouchers and continuous Medicaid coverage on an overall basis. Each case manager has access to an individualised dashboard that includes operational metrics such as new patients to contact, and frequency of patient contacts, timeliness for calling patients recently discharged from the hospital, whether patients have continuous Medicaid coverage, and completion of social risk screenings.

Study recruitment

Semistructured interviews were conducted with 30 field-based case managers as part of the programme’s evaluation and quality improvement process. Participants included four mental health clinical specialists, five substance abuse counsellors, six social workers, nine public health nurses, four housing support specialists and two community health worker specialists. Case managers were recruited by email and selected based on purposive sampling to reflect membership across disciplines and experience working with CommunityConnect for at least 1 year. Three case managers declined to participate. Interviews ended when data saturation was achieved. 23

Interview procedures

Interviews were conducted by five CommunityConnect evaluation staff members (including EEE), who received training and supervision from the evaluation director (EH), who also conducted interviews. The evaluation staff were bachelor and masters-level trained. The evaluation director was masters-level trained and held prior experience in healthcare quality and programme planning.

The evaluation team drafted the interview guide to ask about a variety of work processes and experiences with the goal of improving programme operations including staff and patient experiences. Specific questions analysed for this study were (1) how case managers define success with a patient and (2) examples where case managers considered work with patients a success.

Interviews took place in-person in private meeting rooms at case managers’ workplace from March 2019 – November 2019. Interviews lasted 60–90 min and only the interviewer and case manager were present. All interviewers were familiar with CommunityConnect yet did not have a prior relationship with case managers. Case managers did not receive compensation beyond their regular salary for participating in the study and were allowed to opt out of recruitment or end the interview early for any reason. All interviews were audio recorded, transcribed and entered into Nvivo V.12 for analysis.

Patient and public involvement

This project focused on case manager’s perspectives and thus did not directly involve patients. Rather, patients were involved through case manager recollections of experiences working with patients.

Data analysis

We used an integrated approach to develop an initial set of qualitative codes including deductive coding of programme processes and concepts, followed by inductive coding of how case managers defined success. All interviews were coded by two researchers experienced in qualitative research (EEE and MK). Themes were determined based on recurrence across interviews and illustrative examples and being described by more than one case manager type. The two researchers identified preliminary themes independently, then consulted with one another to achieve consensus on final themes. Themes and supporting quotes were then presented to the full author team to ensure collective agreement that key perspectives had been included. Preliminary results were also shared at a staff meeting attended by case managers and other staff as an opportunity for feedback on study findings. This manuscript addresses the Standards for Reporting Qualitative Research, 24 and the Consolidated Criteria for Reporting Qualitative Research checklist is provided as an appendix. 25

All case manager participants provided informed consent. Research procedures were approved by the Contra Costa Regional Medical Center and Health Centers Institutional Review Committee (Protocol 12-17-2018).

Case managers frequently and across multiple roles mentioned three characteristics of success when working with patients: (1) establishing trust; (2) fostering change in patients’ mindset or initiative and (3) promoting stability and independence. Across these characteristics, case managers expressed that success is patient-defined, with individualised and often incremental progress—a contrast with external perceptions of success and common operational or productivity metrics (see figure 1 ).

An external file that holds a picture, illustration, etc.
Object name is bmjoq-2021-001807f01.jpg

Illustration of key themes.

Success is establishing trust

Trusting relationships were the most widely noted characteristic of success. Trust was described as both a product of case managers’ consistent follow-up and helpfulness over time and a foundational step to enable progress on patient-centred goals. To build trust, case managers explained, patients must feel seen and heard, and understand the case managers’ desire to help: ‘Success is to know that she knows me very well…I look for her on the streets, and I’m waiting for her to call me back. Hopefully she knows that when she’s ready I will be there at least to provide that resource for her and so it’s that personal relationship that you build’ (Case manager 11, social worker). Case managers also highlighted the longitudinal relationship required to establish trust, distinguishing success as more than one-time information delivery or navigating bureaucratic processes to procure services.

Case managers also identified trust as foundational to provide better support for patients: ‘So they’re as honest with me as they can be. That way I have a clear understanding about realistically what I can do to help them coordinate their care or link them to services.’ (Case manager 2, mental health clinician specialist). Establishing trust was essential to improve communication with patients and produced an amplifying effect. That is, a case manager’s initial help and follow-up builds trust so that patients can be more open, and open communication helps the case manager know what specific services can be most useful. This positive feedback loop further cements trust and builds momentum for a longitudinal relationship.

Permission to have a home visit was mentioned as a valuable indicator of early success in building trust: ‘(Your home is) your sanctuary’, expressed one case manager (Case manager 29, public health nurse), acknowledging the vulnerability of opening one’s home to an outsider. For another case manager, regular home visits in the context of a trusting relationship made the case manager aware of and able to address a difficult situation: ‘Every time I was going to her home, I was noticing more and more gnats flying around… She said it’s because of the garbage…’ After establishing trust, the patient allowed the case manager access to the bedroom where the case manager uncovered numerous soiled diapers. The case manager arranged professional cleaning and sanitation through CommunityConnect, after which, ‘there was room for a dance floor in her bedroom. There was so much room, and the look on her face, it was almost as if her chest got proud, just in that day. She didn’t seem so burdened…So that’s a success’ (Case manager 4, substance abuse counsellor). Across multiple examples, case managers expressed trust as a critical element for effective patient partnerships.

However, the pathways to building trust are less clear cut. Quick wins through tangible support such as a transportation voucher to a medical appointment could help engage a patient initially. Yet case managers more frequently emphasised strategies based on relationships over time. Strategies included expressing empathy (putting yourself in the patient’s shoes), demonstrating respect (especially when the patient has experienced disrespect in other health system encounters), keeping appointments, following through on what you say you will do, calling to check in and ‘being there’. Overall, case managers expressed that trust lets patients know they are not alone and sets the stage for future success.

Success is fostering a change in patients’ mindset or initiative

Case managers described a change in patients’ mindset or initiative as evidence of further success. One case manager explained, ‘Really (success) could be a switch in mind state… If I can get someone to consider addressing an issue. Or just acknowledging an issue. That’s progress’ (Case manager 24, substance abuse counsellor). Another case manager spoke to the importance of mindset by stating, ‘what I try to do is not just change the surface of life’. This case manager elaborated, ‘You help (a patient) get their housing and they’re gonna lose it again, unless they change; something changes in their mindset, and then they see things differently.’ (Case manager 6, mental health clinician specialist). Some case managers suggested that the supportive resources they provide are only band-aid solutions if unaccompanied by a changed mindset to address root causes.

Case managers reported that shared goals and plans are essential, in contrast to solutions identified by case managers without patient involvement. ‘I can’t do everything for them’, expressed one case manager (Case manager 21, public health nurse), while others similarly acknowledged that imposing self-improvement goals or providing resources for which a patient may not be ready may be counterproductive. Rather, one case manager emphasised, ‘I think it’s really important to celebrate people’s ideas, their beliefs, their own goals and values’. (Case manager 4, substance abuse counsellor). As an example, the case manager applauded a patient’s ideas of getting a driver’s license and completing an education certificate. In summary, case managers viewed success as a two-way street where patient’s own ideas and motivation were essential for long-term impact.

Success is promoting stability and independence

Case managers also identified patients’ stability and independence as a characteristic of success. One case manager stated, ‘I define success as having them be more independent in their just manoeuvring the system…how they problem solve’ (Case manager 30, public health nurse). Relative to the other characteristics of success, stability and independence more closely built on resources and services coordinated or procured by the case manager. For example, CommunityConnect provides cell phones free-of-charge to patients who do not currently have a phone or continuous service, which has helped patients build a network beyond the case manager: ‘Once we get them that cell phone then they’re able to make a lot of connections … linking to services on their own. They actually become a lot more confident in themselves is what I’ve seen’. (Case manager 23, substance abuse counsellor). In another example, a case manager helped a patient experiencing complex health issues to reconcile and understand various medications. For this patient stability means, ‘when he does go into the emergency room, it’s needed. … even though he’s taking his medication like he’s supposed to… it’s just his health gets bad. So, yea I would say that one (is a success)’ (Case manager 8, social worker). Thus, stability represents maintained, improved well-being, supported by care coordination and resources, even while challenges may still be present.

As a step further, ‘Absolute success’, according to one case manager, ‘(is when a patient) drops off my caseload and I don’t hear from them, not because they’re not doing well but because they are doing well, because they are independent’ (Case manager 12, social worker). Patients may still need periodic help knowing who to contact but can follow through on their own. This independence may arise because patients have found personal support networks and other resources that allow them to rely less and less on the case manager. While not all patients reach this step of sustained independence and stability, it is an accomplishment programmatically and for case managers personally.

Success is patient-defined, built on individualised and incremental progress

Case managers widely recognised that success comes in different shapes and sizes, dependent on their patient’s situation. Irrespective of the primary concern, many identified the patient’s own judgement as the benchmark for success. One case manager explained, ‘I define success with my patients by they are telling me it was a success. It’s by their expression, it’s just not a success until they say it’s a success for them’ (Case manager 7, social worker). In a more specific example, a case manager highlighted checking in with a patient instead of assuming a change is successful: ‘It’s not just getting someone housed or getting someone income. Like the male who we’re working towards reconciliation with his parents… that’s a huge step but if he doesn’t feel good about it… then that’s not a success.’ The same case manager elaborated, ‘it’s really engaging with the knowing where the patient him or herself is at mentally, for me. Yeah. That’s a success’ (Case manager 18, homeless services specialist). This comment challenges the current paradigm where, for example, if a patient has a housing need and is matched to housing, then the case is a success. Rather, case managers viewed success as more than meeting a need but also reciprocal satisfaction from the patient.

Often, case managers valued individualised, even if seemingly small, achievements as successes: ‘Every person’s different you know. A success could be just getting up and brushing their teeth. Sometimes success is actually getting them out of the house or getting the care they need’ (Case manager 28, social worker). Another case manager echoed, ‘(Success) depends on where they’re at … it runs the gamut, you know, but they’re all successes’ (Case manager 10, public health nurse). CommunityConnect’s interdisciplinary focus was identified as an important facilitator for tailoring support to individualised client needs. In contrast with condition-specific case management settings, for example, a case manager with substance abuse training noted, ‘whether someone wants to address their substance use or not, they still have these other needs, and (with CommunityConnect) I can still provide assistance’ (Case manager 24).

However, the individualised and incremental successes are not well captured by common case management metrics. One case manager highlighted a tension between operational productivity metrics and patient success, noting, ‘I get it, that there has to be accountability. We’re out in the field, I mean people could really be doing just a whole lot of nothing… (Yet), for me I don’t find the success in the numbers. I don’t think people are a number. Oh, look I got a pamphlet for you, I’m dropping it off… I don’t think that that is what’s really going to make this programme successful’ (Case manager 8, social worker). One case manager mentioned change in healthcare utilisation as a marker of success, but more often, case managers offered stories of patient success that diverge from common programme measures. For example, one case manager observed, ‘The clear (successes) are nice: when you apply for Social Security and they get it that’s like a hurrah. And then there’s other times it’s just getting them to the dentist’ (Case manager 28, social worker). Another case manager elaborated, ‘It’s not always the big number—the how many people did I house this year. It’s the little stuff like the fact that this 58-year-old woman who believes she’s pregnant and has been living outside for years and years, a victim of domestic violence, has considered going inside. Like that is gigantic’ (Case manager 18, homeless services specialist). Overwhelmingly, case managers defined success through the interpersonal relationship with their patients within patients’ complex, daily life circumstances.

Case managers’ definitions of success focused on establishing trust, fostering patients change in mindset or initiative, and, for some patients, achieving independence and stability. Examples of success were commonly incremental and specific to an individual’s circumstances, contrasting with programmatic measures such as reduction in hospital or emergency department utilisation, benefits and other resources secured, or productivity expectations. Study themes heavily emphasise the interpersonal relationship that case managers have with patients and underscore the importance of patient-centred and patient-defined definitions of success over other outcome measures.

Our results complement prior work on clinic-based programmes for complex patients. For example, interdisciplinary staff in a qualitative study of an ambulatory intensive care centre also identified warm relationships between patients and staff as a marker of success. 26 In another study interviewing clinicians and leaders across 12 intensive outpatient programmes, three key facilitators of patient engagement emerged: (1) financial assistance and other resources to help meet basic needs, (2) working as a multi-disciplinary care team and (3) adequate time and resources to develop close relationships focused on patient goals. 27 Our results concur on the importance of a multi-disciplinary approach, establishing trusting relationships, and pursuing patient-centred goals. Our results diverge on the role of resources to meet basic needs. Case managers in our study indicated that while connections to social services benefits and other resources help initiate the case manager-patient relationship, lasting success involved longer-term relationships in which they supported patients in developing patients’ own goal setting skills and motivation.

An important takeaway from case managers’ definitions of success is the ‘how’ they go about their work, in contrast to the ‘what’ of particular care coordination activities. For example, case managers emphasise interpersonal approaches such as empathy and respect over specific processes and resource availability. Primary care clinicians, too, have expressed how standard HEDIS or CAHPS quality metrics fail to capture, and in some cases disincentivise, the intuitions in their work that are important for high quality care. 28 29 Complex care management programmes must also wrestle with this challenge of identifying standards without extinguishing underlying quality constructs.

Strengths and limitations

This study brings several strengths, including bringing to light the unique, unexplored perspective of case managers working on both health and social needs with patients facing diverse circumstances that contribute to high-risk of future hospital or emergency department utilisation. The fact that our study explores perspectives across an array of case manager disciplines is also a strength, however a limitation is that we are unable to distinguish how success differed by discipline based on smaller numbers of each discipline in this study sample. Other study limitations include generalisability to other settings, given that all case managers worked for a single large-scale social needs case management programme. Comments around productivity concerns or interdisciplinary perspectives on ways to support patients may be unique to the infrastructure or management of this organisation. In addition, at the time of the study, all case managers were able to meet with patients in-person; future studies may explore whether definitions of success change when interactions become virtual or telephonic as occurred amidst COVID-19 concerns.

This study is the first to our knowledge to inquire about holistic patient success from the perspective of case managers in the context of a social needs case management programme. The findings offer important implications for researchers as well as policy makers and managers who are designing complex case management programmes.

Our results identify patient-directed goals, stability and satisfaction, as aspects of social needs case management which are difficult to measure but nonetheless critical to fostering health and well-being. Case managers indicated these aspects are most likely to emerge through a longer-term connection with their patients. Thus, while resource-referral solutions may play an important role in addressing basic needs, 30 our findings suggest that weak patient–referrer rapport may be a limitation for such lighter touch interventions. The need for sustained rapport building is also one explanation why longer time horizons may be necessary to show outcome improvements in rigorous studies. 16

Relatedly, results point to trusting relationships as an under-recognised and understudied feature of social needs case management. Existing research finds that patients’ trust in their primary care physician is associated with greater self-reported medication adherence 31 along with health behaviours such as exercise and smoking cessation. 32 Similar quantitative results have not yet been illuminated in social needs case management contexts, yet the prominence of trusting relationships in this study as well as other sources 26 27 33 34 suggests that measures of trust should be used to complement currently emphasised outcomes such as inpatient and outpatient utilisation. Future research and programme evaluation will need to develop new trust measurement or modify existing trust measures for the social needs case management context. 31 35

In summary, study themes provide waypoints of how to conceptualise programme design, new staff training and potential measurement development for complex case management programmes like CommunityConnect. Despite the broad swath of social needs addressed, case managers coalesced on establishing a trusting relationship as a necessary foundation to appropriately identify needs and facilitate connections. Second, fostering patients’ own ideas, including a change their mindset or initiative, was important to fully make use of programme resources. Third, supporting new-found independence or stability was a gratifying, but not universally achieved marker of success. Commonly, case managers highlighted moments of success with mindfulness toward small victories, illuminating that success is non-linear with no certain path nor single end point. Themes emphasise the importance of bringing compassion for the complexity in patients’ lives and developing collaborative relationships one interaction at a time.

Acknowledgments

The authors would like to thank the CommunityConnect evaluation team for their support conducting and transcribing interviews and applying preliminary coding, especially Gabriella Quintana, Alison Stribling, Julia Surges and Camella Taylor.

Contributors: MK coded and analysed qualitative data, identified key themes and related discussion areas, and drafted and critically revised the manuscript. EEE conducted interviews, coded and analysed qualitative data, and drafted and critically revised the manuscript. EH developed the study instrument, conducted interviews, supervised data collection, contributed to the data interpretation and critically revised the manuscript. MDF contributed to the interpretation and critically revised the manuscript. NS contributed to the interpretation and critically revised the manuscript. ALB contributed to the design and interpretation and critically revised the manuscript. All authors approve of the final version to be published.

Funding: MK was supported by the Agency for Healthcare Research and Quality (AHRQ) under the Ruth L. Kirschstein National Research Service Award T32 (T32HS022241). MDF was supported by the Agency for Healthcare Research and Quality, grant # K01HS027648.

Disclaimer: Its contents are solely the responsibility of the authors and do not necessarily represent the official views of AHRQ. Funding had no role in the study’s design, conduct or reporting.

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Contra Costa Regional Medical Center and Health Centers Institutional Review Committee (Protocol 12-17-2018). Participants gave informed consent to participate in the study before taking part.

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