The Human in the Digital

A Brief History of Digitalization

Digitalization will not replace the human. In a more digital world, the unique human elements will be even more valuable. This applies in particular to our ability to find creative solutions in teams and organizations and to work together effectively and efficiently toward shared goals. When we talk about the human factor in general and humane leadership in particular, we are always asking how we can achieve great things together, especially against the backdrop of complex, global problems such as climate change. Solutions to those challenges will owe much less to individual masterminds, top performers, and heroes than is generally assumed and often portrayed.

Digitalization: hardly any other buzzword has been so overused in recent years. And as with all buzzwords, the frequency of use is inversely proportional to understanding. Digitalization obviously has something to do with computers and computing power. But that alone is not enough; computers have existed for too long. Interconnected devices and the power of platforms make all the difference.

Computers that grow smaller, more powerful, and more suitable for everyday use are obviously essential to digitalization. In 1965, Gordon Moore predicted that the computing power of computers would double every year.1 The pace has slowed somewhat (to a doubling every 18 months), but Moore’s law still holds today.

Most people understand this exponential growth in computing power rationally. Yet when we imagine the future, we project the past linearly. We look at what has changed in the past five years and assume the next five will bring the same. They won’t.

A thought experiment shows the error. The movie “Back to the Future” sends its hero in time between 1985 and 1955, and part of the comedy comes from how much the world changed technologically in those thirty years. Make the same thirty-year leap between 1985 and 2015, and Marty McFly would be utterly lost. In those thirty years, the first mass-produced home computers, such as the famous Commodore C64, turned into the constantly networked smartphone, which is also a camera, navigation device, Walkman, portable TV, and much more.

Digitalization is more than sheer computing power. It lets us create, convert, and edit all kinds of information digitally. At first, however, this happened only locally, on isolated computers. The music industry itself pushed digitalization ahead by digitizing analog music in the form of CDs.

MP3 alone was not a problem for the music industry. The problem began when computers were networked and file-sharing platforms such as Napster appeared, later replaced by streaming services such as Spotify. Digital information that had been available only locally was suddenly accessible to everyone, anywhere, at any time. Networked devices are the basis, but platforms make the difference.

In 2007, Nokia, with its market dominance in mobile phones, and RIM, with the very successful Blackberry, had first-class products that were technically superior to the first iPhone. But the iPhone focused consistently on the mobile Internet. Earlier mobile phones could also do email and, more or less, the Internet. The iPhone made a permanent Internet connection its key differentiator and became a fully fledged smartphone.

Even that wouldn’t have been enough, because at the time there were few use cases for the mobile Internet besides email and web browsing. The decisive difference was Apple’s App Store (and the corresponding Google Play Store for Android). Apple and Google turned the smartphone into a more or less open platform for third-party applications. Apple also made significant profits from the App Store, but the main point is that every new app made the iPhone more valuable.

Since then, ubiquitous networking has spawned ever more digital platforms on which supply and demand can meet. Digitalization thus reaches into areas that at first appear less digital. Uber attacks the business model of taxi companies without owning a single vehicle, and it does so worldwide thanks to digitalization. Airbnb competes with established hotel chains without owning a single hotel, again all over the world. That is the real digitalization.

Here, I Am Human—Here, I Can Be Curious!

Digitalization is displacing human labor from the value chain. It simply continues what industrialization, robots, and automation started long ago. One can feel threatened by this or see an opportunity. Digitalization is finally bringing unique human skills back into focus. It is a chance to re-humanize business and to see the whole human instead of just a workforce.

All of us were curious once. We were born that way. My children constantly ask why and question everything and everyone, and that’s a good thing. It would be even better if we didn’t lose this curiosity over time by being taught not to ask so many questions.

School reinforces this tendency. Instead of harnessing and encouraging innate curiosity, it focuses on imparting factual knowledge and skills and testing students’ ability to regurgitate them. “Education is not the filling of a pail, but the lighting of a fire.” If schools followed this advice from Plutarch, students would learn to ask the questions and work out the answers with the teacher’s help. Usually it is the other way around: the teachers ask the questions, and the pupils give the answers they have learned.

Still, the school system only follows the principle of supply and demand. For decades, the economy has demanded standardized “human material” for set roles and job descriptions, and “asking stupid questions” is not part of them. On the contrary, those who question too much only disrupt the process. Fortunately it is rarely put so drastically, but in many places Vilos Cohaagen’s line in the movie Total Recall still applies in principle: “Who told you to THINK? I don’t give you enough information to THINK! You do as you’re told, THAT’S WHAT YOU DO!”

This kind of socialization and training immediately explains the fear of automation, digitalization, and artificial intelligence. People who have been degraded to cogs in a massive organizational machine are justifiably afraid that an algorithm will soon take over their function. But this is also an opportunity for people in companies to develop as human beings again instead of remaining standardized workers. Or, to paraphrase Johann Wolfgang von Goethe: Here I am human–here I can be curious!

One day, machines and computers might find better answers and solve problems better than humans. But only humans, with their innate curiosity, can ask the right questions. This constant questioning is the crucial human core competence in a world that changes ever faster. We now “only” have to restructure and manage organizations so that they nurture this curiosity.

The more complex the problems become, the more critical our human creativity becomes. Complexity does not mean greater difficulty; it means a different category of problems and situations.

The Whole and Its Parts

The Whole and Its Parts

Is a mechanical clockwork complicated? Or is it complex? And the human brain? Is it complex or complicated? Or maybe both? When we don’t (immediately) understand something, we call it complicated, and when it is very complicated, we call it complex. But is that correct, or does our sense of language mislead us?

Let us approach this question from familiar territory. The boundary between the obvious and the complicated is the horizon of one’s knowledge. Whether someone sees something as obvious or complicated always depends on what they know. A mechanical clockwork is undoubtedly complicated for me, but not for a watchmaker. With computers, it is probably the other way around. In Dave Snowden’s Cynefin framework, the boundary therefore runs between the “known knowns” and the “known unknowns.”2 A complicated situation can be understood in principle, and answers exist. They may not be the standard answers or best practices, though; finding them can require deeper expert analysis.

If my car suddenly starts running erratically, I first go through the obvious checks of a layman: Is there enough fuel? Do all the tires have enough air? If none of this solves the problem, my knowledge and experience are insufficient, and I must see a mechanic. If my computer runs slowly, I go through the list of processes, kill one or two, or restart the computer (for decades the best practice par excellence with Windows). If none of that helps, I need the advice of an expert (or a Mac).

But how do I recognize that I’ve reached my wit’s end and should consult an expert? How do I know what I know and don’t know? Can I admit to myself that a situation is beyond my capabilities? Humans are reluctant to answer these tricky questions honestly.

Based on a basic initial understanding, we tend to overestimate ourselves dramatically. Our incompetence is fundamentally unconscious, as David Dunning explains:3 “If you are incompetent, the skills you need to give a correct answer are the very skills you need to recognize what a correct answer is.” This Dunning-Kruger effect, named after its two discoverers,4 favors fruitless trial and error with panaceas in situations that long ago called for deeper analysis by real experts.

Although the words are often used interchangeably in everyday language, complicated is not the little sister of complex. The difference is as fundamental as the difference between a Ferrari and the rainforest. The Ferrari is a complicated device. It can be disassembled and, crucially, understood analytically through the individual functions of its components. Its behavior is deterministic: when I press the accelerator, the relevant components interlock as planned and accelerate the car. If they don’t, the car is broken and needs repair. Essentially, the whole is the sum of its parts. The behavior of a complicated system is predictable and unsurprising.5

The rainforest also consists of many individual elements and influencing variables, but the dynamics between them are entirely different from those in the Ferrari. That is precisely the difference. A system becomes complex when the quantity, arrangement, and relationships of its largely autonomous elements are constantly in flux.6 Its behavior can no longer be explained by its components; patterns emerge from their interaction:7 “Its performance is never equal to the sum of the actions of its components; rather, it is a function of their interactions.”

The Cynefin framework describes complex situations as the domain of the “unknown unknowns.”8 We do not know what we do not know and must first explore what there is to know. Cause and effect can be understood only in retrospect and macroscopically, from patterns of behavior, not analytically and microscopically from the individual elements. Neuroscientists understand the structure and functioning of the brain very well, yet they are no better than I am at predicting thoughts or actions. Psychological experiments can nevertheless demonstrate thought patterns at the macroscopic level, such as the Dunning-Kruger effect mentioned above.

The boundary between complicated and complex therefore runs between predictable and surprising, static and dynamic, and ultimately dead and alive. Dealing successfully with complexity requires a change of method from analytics to empiricism. We can no longer determine behavior and causalities analytically, by breaking them down into components. We can only explore, understand, and describe them with suitable hypotheses and experiments that verify or falsify them.

People carry out complicated projects. This human factor quickly turns a technically challenging problem into a complex situation. Because people are involved and affected, a project is almost always complicated and complex at the same time.

Companies consist of many people and functional units and make products for global markets, which in turn consist of many players and are subject to many influences. The vast majority of situations are therefore complex, or at least have significant complex components. Comprehensive analysis helps only to a limited degree. We need an empirical approach that quickly validates or falsifies ideas in the real environment.

This is the sweet spot of agility, with its short feedback loops. Teams deliver product increments at short intervals and, ideally, test them on the market immediately to learn what works and what doesn’t. Learning and improvement concern not only the what but also the how, so agility also means continuously improving how the people involved collaborate. With these two learning loops, agility offers a sound basis for dealing with complexity: in collaboration between people on the one hand, and in interaction with the market and customers on the other.

When the Method Becomes the Problem

Abraham Maslow wrote in 1966, “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.”9 This tendency to apply tools and methods to any problem, however unsuitable, simply because they are at hand is known as the “Law of the Instrument” or “Maslow’s Hammer.” Combined with the Dunning-Kruger effect, it explains many aberrations in agile transformations, such as committees that suddenly organize their work in sprints even though they are neither a team nor working on a joint product.

Maslow’s hammer is not a problem for laypeople, however. It describes a blind spot of experts. The blind spot becomes painfully noticeable when experts who mainly work on complicated things face complex issues. Engineering, for example, is mostly concerned with building something complicated. The approach is therefore analytical: experts break down the problem, analyze the parts and various aspects, and then put the solutions together. This is how we build factories, cars, and airplanes.

Regular airplanes, at least.

By 1976, the art of aircraft construction was well advanced. That year, the Concorde became the first supersonic passenger aircraft to enter regular service. It carried its passengers from London or Paris to New York at more than twice the speed of sound in a record 3 to 3.5 hours, twice as fast as before. Yet a different and, at first glance, much simpler challenge of aircraft construction was still unsolved.

In 1959, preliminary development of the Concorde began in France and Great Britain. In the same year, the British industrialist Henry Kremer donated a prize of 5,000 British pounds for the first human-powered aircraft to fly a horizontal figure eight around two posts half a mile (806 meters) apart within 8 minutes. These were the rules of the Kremer Prize. In 1967, Kremer doubled the prize money, and in 1973 he raised it to 50,000 British pounds. Despite this impressive sum, equivalent to around 780,000 US dollars in today’s purchasing power, many teams failed to solve the problem over the years.10

The American physicist Paul MacCready had a doctorate in atmospheric disturbances and was a passionate glider pilot, but he was not an aircraft engineer. His experience was limited to building indoor model airplanes in his youth and flying hang gliders with his sons. In the summer of 1976, he was 100,000 US dollars in debt after guaranteeing a friend’s failed start-up. At the exchange rate of the time, that sum corresponded almost exactly to the 50,000 British pounds of the Kremer Prize, which is why MacCready became interested in human-powered flight.

MacCready knew nothing of the “right” way to design airplanes, and he had no budget for a large team of experts and expensive equipment. So he did not spend much time analyzing and planning like the other professional teams. Having studied the flight of vultures during his summer vacation, he decided to try his luck with a lightweight “model aircraft” with an enormous wingspan of 29 meters, about that of a DC‑9. Within two months, the first version of the Gossamer Condor, made of aluminum tubes, wire ropes, and rigid foam covered with polyester film, was ready for a test flight. It ended, like so many afterward, in a crash.

But that was precisely the point.

The Gossamer Condor was the naïve work of an amateur who did not care how professionals built airplanes according to the state of the art. That state of the art produced very nice-looking and relatively fast airplanes, but also quite complex and heavy ones–too heavy to fly for long on human muscle power alone. The real competitive advantage of MacCready’s design was not its lightness or other technical refinements but that the Gossamer Condor was simple to build and repair. This allowed his team to learn from failures faster than the competition.

The tactic soon paid off. Within a few months, MacCready’s small team overtook the competition, improving the Gossamer Condor from failure to failure. On August 23, 1977, with the professional cyclist Brian Allen as pilot, it flew the figure eight around the two posts half a mile apart in a relatively leisurely 7:25:05 minutes. Just two years later, on June 12, 1979, the same team crossed the English Channel with the Gossamer Albatross, the Condor’s successor, and won the second Kremer Prize, worth 100,000 British pounds.11 MacCready was out of debt and went down in the annals of aviation.

The Gossamer Albatross II on a test flight at NASA's Dryden Flight Research Center in Edwards, California.

The Gossamer Albatross II on a test flight at NASA's Dryden Flight Research Center in Edwards, California (Source: NASA).

The professional teams before him followed the rules of engineering. If that art made supersonic flight and the moon landing possible, surely it could solve this seemingly simple problem. But the problem was more complex than it looked. The analytical approach that had improved aircraft construction over decades could not deal with the complexity of human-powered flight.

MacCready followed his instincts and approached the problem more empirically than his analytical competitors, also for lack of alternatives. He concentrated his minimal resources on the essentials and omitted everything else. The airplane didn’t have to be fast or nice-looking. It needed a large wingspan for lift and as little weight as possible, because human muscle power was the limiting factor. To learn quickly from experiments and try out modified designs, it had to be simple to build and easy to repair. Greg McKeown summarizes the approach with quotes from a lecture MacCready gave at MIT:12

The real challenge was not to build an elegant plane that could fly the figure eight around the two posts on the field but to develop a large, lightweight plane, “no matter how ugly it is,” that could be “repaired, modified, changed and redesigned again after a crash—and quickly.” At that moment, he suddenly realized: “There’s an easy way to do it.”

Anyone who has spent years of education and professional life successfully using a hammer on different nails will find it hard to recognize the screw and change tools. At first glance, the Kremer Prize looked like a rather complicated problem–or at least the experienced engineers thought so. In reality, one restriction dominated the complexity: the aircraft had to be powered solely by a person’s muscles. Only the empirical approach of trial and error allowed MacCready, a layman, to do what the experts before him had been denied for so long.

The Eighth Type of Waste

The Eighth Type of Waste

MacCready’s story also shows how much untapped potential people tend to have. Had his debts not motivated him, he might have just done his regular job well and built a few model airplanes with his children in his spare time.

The extraordinary story of the first BMW 3 Series Touring is similar. In 1984, Max Reisböck had a problem. His employer’s brand strategy did not suit his family’s needs: BMW deliberately had no hatchback in its model catalog, and the notchback 3 Series was too small for his growing family. So Reisböck, a trained car body maker who had worked in prototype construction at BMW for several years, quickly turned an E30 notchback saloon into the first BMW 3 Series Touring13 in his spare time. Unfortunately, this remarkable achievement did not solve his problem in the short run. When he presented the prototype at the headquarters in Munich, Eberhard von Kuehnheim, then Chairman of the Board of Management, and Wolfgang Reitzle, his Head of Development, immediately recognized its potential. They ordered strict secrecy until the car was ready for series production: “This car will not leave the company grounds.”14

Lean Management knows seven classic types of waste. As the examples of MacCready and Reisböck show, it is quite right to add an eighth: the unused potential of employees. Harnessing human potential and creativity for continuous improvement is a key principle of Lean Management, so this eighth type of waste underlies and complements the other seven.

The rise of Toyota after the Second World War is inextricably linked with the name of Taiichi Ohno. He was instrumental in shaping and developing the Toyota Production System, and today’s Lean Manufacturing and, more generally, Lean Management can largely be traced back to him. Toyota significantly increased its productivity, caught up with its American competitors from Detroit, and then overtook them. The concepts and methods of Lean Management spread through manufacturing and many other sectors, including IT, where the “Manifesto for Agile Software Development”15 can be understood as applying Lean principles to software development.

In many factories, overproduction of semi-finished and finished products was common. These buffers compensate to a certain extent for quality fluctuations in production (and in the supply chain) and protect quality and delivery dates. Because the buffers are so practical and seemingly indispensable, this type of waste often goes unrecognized. Yet the hidden costs of inventory, storage space, and transportation are easy to underestimate.

A central element of Lean Management is therefore the elimination of waste. That means avoiding wasteful activities, i.e., activities that generate no value. This is a better translation of the Japanese term muda than “waste.” Traditionally, there are seven types of muda, as shown in this graphic:

The seven types of waste (muda) in Lean Management.

The seven types of waste (muda) in Lean Management

These seven types all relate to processes. They are symptoms of weaknesses in work processes, whose causes must be found and eliminated. Continuous process improvement, known in Japanese as Kaizen, is an essential element of the Toyota Production System. In contrast to the Tayloristic view prevailing at the time, however, Lean Management does not reserve continuous improvement for the manager. It is the task of the “ordinary” workers–a small but significant difference.

Avoiding the seven types of waste requires the creativity of everyone involved in the processes. Managers’ central task is to empower these previously “ordinary” workers and train them to do it. In essence, Ohno was concerned with developing untapped human potential.

To emphasize this, we often call unused human potential or unused creativity the eighth type of waste. Rightly so, because organizations are still run like machines, and the people in them are treated like cogs whose skills are not in demand beyond their current job description. This eighth type of waste certainly differs in character from the other seven, but utilizing creative potential is so crucial in Lean Management that adding it to the seven classic types makes a lot of sense.

It pays off when human resources finally become human potential.

Respect for People

Lean Management has much to teach us: understand the value for the customer, identify the value stream, optimize the flow through the system to avoid unnecessary effort, and, last but not least, ensure continuous improvement. But Lean Management is not just about different and better methods. It is also about a different management culture. Respect for people is an essential pillar of the Toyota Way16 that is often forgotten. People are at the heart of Lean Management, so the central management task is to “empower instead of instruct.” This principle deserves to be spread as intensively as the well-known methods of Lean Management.

Toyota’s extremely successful turnaround cannot be explained by a brilliant engineer inventing and introducing a few groundbreaking concepts, as the heroic story of Taiichi Ohno might suggest at first glance. The foundation of the transformation was a change in management culture that put people and their creativity front and center. The individual worker was no longer a passively affected object but an actively shaping subject, as Ohno made clear:17 “Standards should not be forced down from above but rather set by the production workers themselves.”

Yoshihito Wakamatsu, who worked directly under Ohno for many years, reported an anecdote that shows how seriously Ohno took this empowerment:18 During a visit to a Toyota plant, Ohno was accompanied by a manager from another company. The manager noticed some things that could have been improved in the implementation of the Toyota Production System and asked Ohno why he had not corrected them immediately. Ohno answered:

I am being patient. I cannot use my authority to force them to do what I want them to do. It would not lead to good quality products. What we must do is to persistently seek understanding from the shop floor workers by persuading them of the true virtues of the Toyota System. After all, manufacturing is essentially a human development that depends heavily on how we teach our workers.

This response is an excellent example of the attitude of humane leadership, which is less about correcting, guiding, or instructing than about empowering. Correcting processes from the outside would cure the symptoms only in the short term and would not produce a healthy organization.

Empowerment made the individual worker an active shaper of continuous improvement, and the resulting broad impact made the decisive difference in Toyota’s transformation. We need to recall this today, when many companies reduce agile transformations to introducing blueprints and frameworks.

The ‘e’ in Lean and Agile therefore stands for empowerment, or at least it should. Unfortunately, both philosophies, which are related and build on each other, are often reduced to visible practices, methods, and tools. Anyone who introduces these without simultaneously working on the foundations of the image of humankind and the leadership attitude is building on sand. Richard Feynman aptly describes the result of such a reduction as a “cargo cult,” the beautiful imitation (in his case of scientific work) without a deeper understanding of the purpose and the bigger picture:19

In the South Seas, there is a cargo cult of people. During the war they saw airplanes land with lots of good materials, and they want the same thing to happen now. So they’ve arranged to imitate things like runways, to put fires along the sides of the runways, to make a wooden hut for a man to sit in, with two wooden pieces on his head like headphones and bars of bamboo sticking out like antennas–he’s the controller–and they wait for the airplanes to land. They’re doing everything right. The form is perfect. It looks exactly the way it looked before. But it doesn’t work. No airplanes land. So I call these things cargo cult science because they follow all the apparent precepts and forms of scientific investigation, but they’re missing something essential because the planes don’t land.

The self-organizing team is at the heart of Agile. The principles behind the Manifesto for Agile Software Development call for it explicitly:20 “The best architectures, requirements, and designs emerge from self-organizing teams.” And to leave no doubt about the leadership attitude that goes with it, they also say: “Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done.” There is no agile team without self-organization and empowerment, even if frameworks and blueprints have been copied and rolled out by the book and the processes are celebrated. As long as Agile is imposed on people from above, without the necessary empowerment and change of leadership attitude, it will end in a soulless cargo cult.

As with Toyota and Lean Management, the key to a successful agile transformation lies much less in choosing or designing the right frameworks, processes, methods, or tools–an exercise that, unfortunately, appeals greatly to managers and consultants–than in empowerment and self-organization. We must see employees as active subjects of the transformation and, ultimately, of their organization, instead of degrading them to passive objects and mere target groups of intrusive change management measures.

Super Chicken

The human element in a digital world is not just an individual matter but a question of culture and organization. A group of super talents does not make a good team. On the contrary, many strong egos can make a team dysfunctional. This applies not only to soccer teams but also to chickens.

In her TED talk “Forget the pecking order at work,”21 Margaret Heffernan reports an impressive experiment. William Muir of Purdue University investigated the productivity of chickens, which can be measured simply by the number of eggs. For the first group, he selected only the top performers and let only the best of these super chickens reproduce. A second group of average chickens, neither selected nor otherwise influenced, served as the control. After six generations, the chickens in the control group were well fed and fully feathered, and their productivity had increased slightly. Contrary to naïve expectations, the super chickens fared differently: all but three were dead, pecked to death by the others.

At first glance, the explanation for this surprising outcome is quite bland. The super chickens’ higher productivity went hand in hand with their ability to assert themselves against others at the food bowl. Selecting precisely these individuals reinforced aggression and competitive behavior from generation to generation. Those who fight each other may prevail as individuals, but as a group they waste energy. An extreme focus on individual excellence encourages competition and dysfunctional teams. Unfortunately, companies, school systems, and entire societies are built on this principle.

Google also realized that superstars alone do not make a team. In “Project Aristotle,” the company investigated what turns a group of people into an effective team. By far the most important element turned out to be psychological safety.22 In effective teams, members feel safe enough to speak their minds openly and take risks. This is the crucial ingredient that makes the whole more than the sum of its parts, so fostering psychological safety is an essential leadership task. Excellent ideas need this sense of security and trust, as Margaret Heffernan explained in her TED talk with a beautiful analogy:23

And that’s how good ideas turn into great ideas because no idea is born fully formed. It emerges a little bit as a child is born, kind of messy and confused but full of possibilities. And it’s only through the generous contribution, faith and challenge that they achieve their potential.

  1. Gordon E. Moore, “Cramming More Components onto Integrated Circuits, Reprinted from Electronics, Volume 38, Number 8, April 19, 1965, Pp.114 Ff.,” IEEE Solid-State Circuits Society Newsletter 11, no. 3 (2006): 33–35, https://doi.org/10.1109/N-SSC.2006.4785860. ↩

  2. David J. Snowden and Mary E. Boone, “A Leader’s Framework for Decision Making,” Harvard Business Review, November 2007, https://hbr.org/2007/11/a-leaders-framework-for-decision-making. ↩

  3. Errol Morris, “The Anosognosic’s Dilemma: Something’s Wrong but You’ll Never Know What It Is (Part 1),” Opinionator (blog), June 20, 2010, https://opinionator.blogs.nytimes.com/2010/06/20/the-anosognosics-dilemma-1/. ↩

  4. Justin Kruger and David Dunning, “Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments.,” Journal of Personality and Social Psychology 77, no. 6 (1999): 1121–34, https://doi.org/10.1037/0022-3514.77.6.1121. ↩

  5. Snowden and Boone, “A Leader’s Framework for Decision Making.” ↩

  6. Mark Lambertz, Die intelligente Organisation: das Playbook für organisatorische Komplexität, 2. Auflage (Göttingen: BusinessVillage, 2019), 40f. ↩

  7. Russell L. Ackoff, “Systems Thinking and Thinking Systems,” System Dynamics Review 10, no. 2–3 (June 1, 1994): 180, https://doi.org/10.1002/sdr.4260100206. ↩

  8. Snowden and Boone, “A Leader’s Framework for Decision Making.” ↩

  9. A.H. Maslow and John Dewey Society, The Psychology of Science: A Reconnaissance, Gateway Edition (Harper & Row, 1966). ↩

  10. “Kremer-Prize,” on Wikipedia, February 9, 2024. ↩

  11. “Kremer-Prize.” ↩

  12. Greg McKeown, Effortless: Make It Easier to Do What Matters Most, First edition (New York: Currency, 2021), 126. ↩

  13. Wolfram Nickel, “BMW 3er Touring E30: Und plötzlich waren Kombis cool - WELT,” DIE WELT, September 27, 2017, https://www.welt.de/motor/fahrberichte-tests/oldtimer/article168818064/Und-ploetzlich-waren-Kombis-cool.html. ↩

  14. Max Reisböck himself tells this story best in this official BMW video (in German) Max Reisböck BMWstory. Erfinder des BMW 3er Touring., 2015. ↩

  15. Beck et al., “Manifesto for Agile Software Development.” ↩

  16. Toyota Motor Corporation, “Toyota Way 2020 / Toyota Code of Conduct,” Toyota Motor Corporation Official Global Website, 2020, https://global.toyota/en/company/vision-and-philosophy/toyotaway_code-of-conduct/index.html. ↩

  17. Taiichi Ohno, Toyota Production System: Beyond Large-Scale Production (CRC Press, 1988), 98. ↩

  18. Yoshihito Wakamatsu, The Toyota Mindset: The Ten Commandments of Taiichi Ohno (Enna Products Corporation, 2017). ↩

  19. Richard Feynman, “Cargo Cult Science,” Engineering and Science 37, no. 7 (June 1974): 10–13. ↩

  20. Beck et al., “Manifesto for Agile Software Development,” https://agilemanifesto.org ↩

  21. Margaret Heffernan, “Forget the Pecking Order at Work,” https://www.ted.com/talks/margaret_heffernan_forget_the_pecking_order_at_work ↩

  22. Charles Duhigg, “What Google Learned From Its Quest to Build the Perfect Team,” The New York Times, February 25, 2016, sec. Magazine, https://www.nytimes.com/2016/02/28/magazine/what-google-learned-from-its-quest-to-build-the-perfect-team.html ↩

  23. Heffernan, “Forget the Pecking Order at Work.” ↩