Clay Christensen

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The Kink or How Trajectories Bend

The current widespread focus on the problems of replication in scientific studies—<a href='http://www.vox.com/2016/7/14/12016710/science-challeges-research-funding-peer-review-process'>especially those of the social and biomedical sciences</a>—has been to invest in ever more sophisticated analytical tools and techniques: more data, more sophisticated tools of analysis, faster computing, etc. All of these efforts have missed an alternative solution: causality. Most research in biomedical and social science begins with data analysis. But data analysis alone is not capable of yielding a genuine causal model. Essentially, all analysis is subject to the problem of induction—roughly, correlation is not causation. 

Our theory-building model is founded on building a causal model and testing that model in a variety of situations. The most obvious risk for such a process is that having built a model, people tend to unwittingly find corroborating evidence as they apply their model—confirmation bias. However, in our model, a theory is not tested in other environments to confirm it, but to find anomalies for the theory. Anomalies help to refine the causal model both by bounding the model and by working out the conditions of its application. Gravity looks different under different conditions—on the moon vs. the earth vs. open space—but is still the same mechanism. Sometimes we can explain phenomena without needing to refer to gravity—such as explaining superconduction. It’s not that the law of graviton is suspended; it’s just that the law has nothing to add to our understanding of lots and lots of phenomena.  

While an anomaly focus is crucial for improving a causal theory, correlational modes have trouble with “outliers.” Most often outliers are dropped from consideration. But whether to do so relies on extra-statistical thinking. A causal theory must account for any apparent anomaly—resulting in clarification, rejection or bounding of the original theory. Confirmation bias simply cannot enter into such reasoning, since corroborating evidence is interesting, but does not lead to better theory.

Put simply, an anomalies-first focus in theory building either forces biases out or prevents biases from entering into a researcher’s reasoning.


How an Anomaly Focus Eliminates Bias

"I first met Paul LeBlanc when he was working towards his PhD in English Literature at Amherst College. He had chosen an arcane topic for his dissertation: Teaching by computers. Upon graduation he landed a faculty spot at Springfield College, with the scope to continue studying computers and education. Paul then was hired in about 1994 by one of the major publishing companies to head an effort to make books available to students by computer. After three years of trying, the company shut down the effort.

Paul then took his credentials to become president of Marlboro College, a tiny liberal arts college in Vermont, between 1996 and 2003. He guided the college to offer the first graduate degrees college completely online. Paul invited me to be the commencement speaker for the pioneers at Marlboro. There were about 20 of them. It was the first time that any of the graduates had met their classmates in person! In 2003 LeBlanc became president of Southern New Hampshire University – another small private liberal arts college that struggled annually to pay its bills. As he had done in Marlboro, LeBlanc led the establishment of online courses and degrees. In the decade since 2003, enrollment increased from 2,900  to 35,000. Revenues grew from $75 to $220 million per year. The SNHU endowment of $17 million (which generates about $1 million annually from investing the endowment) is dwarfed by the $35 million in cash flow each year generated by the online offerings of SNHU.

The journalists who follow Paul LeBlanc have concluded that Paul finds and follows theories assiduously. Three of the most prominent theories that guide him are disruption, jobs-to-be-done, and business model change. The theories allowed Paul to see the future of higher education more than a decade ahead of anyone else. Though his competitors had access to the same data he did, they lacked a theory. And without a theory, data has no voice.
"

Following Good Theory: Paul LeBlanc

Today, I’m posting something I wrote earlier this week. This is an adaptation of work that my long-time collaborator and friend, Clayton Christensen, and I did together. (It was his birthday Monday--4/6--so this topic drifted to the top of my mind.) To be clear, this is not the original, but an adaptation of our joint work. 

**Anomalies Wanted: Models must be accountable to reality**

In 1994, the US passed the Violent Crime Act. At the time, there was a belief, based on observational study, that longer penalties would deter violent crime. Since that time, the US has become the leader in incarceration rate in the world (655/ 100,000); the collective cost of incarceration has quadrupled (~$16B - ~$70B—direct expenditures for the states, does not include federal expenditures or costs of healthcare, courts/parole systems and increased policing.); and the payoff is modest. (See graph below. Source data are here: http://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm)

![text](https://s3.amazonaws.com/strapi.dx.mba/89dacf1918e74d5980f98e859f004ec4.jpeg)

As you can see from the graph, there are two immediate problems with the 1994 Violent Crime Act. First, the downward trend in violent crime *predates* the Act. Second, this was an incredibly expensive intervention--in both financial and human terms--given its modest outcomes.

The biggest problem, though, isn’t just that the study was wrong, but that it wasn’t accountable to updated information. 

Models can end up in what I refer to as a modeling “cul de sac.” Teacher evaluations done by a number of companies, for example, are often “black boxes” that attempt to estimate the quality of teachers mostly by evaluating the before and after pictures of students in their classes, using “objective” measures like standardized tests.

Among the many problems with most of these evaluations is that it’s not possible to see if they are wrong. Many teachers receive wildly different scores from year to year. Is it really possible that a teacher’s performance is that variable? Of course not. But this is ignored because the models are developed and updated without an external check on their soundness. 

The reason you and I—though we may complain periodically—rely on weather forecasts—often in 15-minute increments—is because weather models have continued to improve, due to their unavoidable clash with reality. If today’s model predicted 57 degrees at 1000A, but it was 58 degrees, the model can be improved.

With credit to Karl Popper, this is the central insight of Clay’s and my work on this issue: anomalies for our models or theories are the most powerful engine for progress. What distinguishes science from superstition is that scientific claims can, in principle, be falsified. 

Finding anomalies (and taking them seriously—that is, not casting them aside as “outliers”) for our statistical, intuitive or causal models also helps us to understand the conditions and boundaries of their application. Without the “failure” of the Michelson-Morley experiments, Einstein wouldn’t have worked on the “puzzles” about the speed of light. Without Brahe’s careful examination of the movement of planets, Kepler would never have scrapped “epicycles” and advanced the theory of elliptical orbits. 

The lesson for all of us right now, thinking about models of the spread of nCoV-19, is that our mental models and the statistical models of what’s happening need to be susceptible to outcomes contrary to prediction. And we should treat those anomalies not as unwelcome intruders, but as treasures that can drive better predictions and deeper insights.

Anomalies Wanted: Models must be accountable to reality

As an example, if we look at the PC market, early PCs could, say, do word processing, but it was cumbersome and the early machines weren’t particularly stable. In the next phase, we saw Compaq and a few others that sold machines that were more stable, more reliable. Eventually, as PCs became reliable, we saw the emergence of Dell, whose value propositions was convenience and customizability in ordering, delivery and service. This finally gave way to companies like Acer who gained market share on the basis of lower prices. 

By using this lens, a company can see where they are in the evolution of a market and plan accordingly.

As a market matures, the basis of competition among products changes. There are four stages. First, in the early stages, customers are looking for a solution that works. We call this functionality. Next, customers are focused on the quality or reliability of a solution. This is generally a larger portion of the market than the functionality phase. Third, the mainstream of the market is looking for customization or convenience in using and acquiring the solution. Finally, not surprisingly, the last phase is all about price. 

As an example, if we look at the PC market, early PCs could, say, do word processing, but it was cumbersome and the early machines weren’t particularly stable. In the next phase, we saw Compaq and a few others that sold machines that were more stable, more reliable. Eventually, as PCs became reliable, we saw the emergence of Dell, whose value propositions was convenience and customizability in ordering, delivery and service. This finally gave way to companies like Acer who gained market share on the basis of lower prices. 

By using this lens, a company can see where they are in the evolution of a market and plan accordingly. 	

Basis of Competition.pdf

Basis of Competition - thumb.jpeg

As a market matures, the basis of competition among products changes. There are four stages. First, in the early stages, customers are looking for a solution that works. We call this functionality. Next, customers are focused on the quality or reliability of a solution. This is generally a larger portion of the market than the functionality phase. Third, the mainstream of the market is looking for customization or convenience in using and acquiring the solution. Finally, not surprisingly, the last phase is all about price. 

Basis of Competition

Most often we have learned about some of the components of the scientific method—hypotheses, testing, etc. Developed through thorough study of the history of science and decades of active application in research, we here present an operating model of the scientific method. 

Put another way, the scientific method is a process for developing and improving our understanding of our world. Harnessed to the overarching goals of product science, it tell us what to do when you don’t know what to do.

The scientific method is the foundation for product science, just as it is for any scientific discipline. It is the method that has led to the progress of the modern world. Understanding what it is and how it works enables one to create knowledge from observations, while keeping a rigorous skepticism about what is observed.

Most often we have learned about some of the components of the scientific method—hypotheses, testing, etc. Developed through thorough study of the history of science and decades of active application in research, we here present an operating model of the scientific method. 

Put another way, the scientific method is a process for developing and improving our understanding of our world. Harnessed to the overarching goals of product science, it tell us what to do when you don’t know what to do.

Scientific Process.pdf

Scientific Process - thumb.jpeg

The scientific method is the foundation for product science, just as it is for any scientific discipline. It is the method that has led to the progress of the modern world. Understanding what it is and how it works enables one to create knowledge from observations, while keeping a rigorous skepticism about what is observed.

9aa01c1408fe4a2497bbab5f63ce5c0c.jpeg

Scientific Process

As an example, consider Microsoft Excel. Its capabilities outstrip the vast majority of its users. This means that the least demanding customers are paying for functionality they don’t need. When an incumbent firm’s product overshoots the mainstream part of the market, this sets the stage for an entrant to offer a cheaper, lower-functionality product. Excel long ago overshot the mainstream market, opening the way for Google Sheets to appeal to users who are satisfied with a “worse” product. Over time Google Sheets has added functionality, encroaching on more and more of Microsoft’s market. 

Generally speaking, a disruptive entrant must use a different business model from the incumbents. They also tend to have inherent cost advantages. By using a different business model and cost structure, disruptive entries take hold among the least demanding customers and generally pursue better margins by moving up-market.

A Disruptive Market Entry is one where a firm entering a market chooses not to compete with existing firms for their best customers. Instead, in a disruptive market entry, entrants appeal to customers who have been “overshot” by incumbents.

As an example, consider Microsoft Excel. Its capabilities outstrip the vast majority of its users. This means that the least demanding customers are paying for functionality they don’t need. When an incumbent firm’s product overshoots the mainstream part of the market, this sets the stage for an entrant to offer a cheaper, lower-functionality product. Excel long ago overshot the mainstream market, opening the way for Google Sheets to appeal to users who are satisfied with a “worse” product. Over time Google Sheets has added functionality, encroaching on more and more of Microsoft’s market. 

Generally speaking, a disruptive entrant must use a different business model from the incumbents. They also tend to have inherent cost advantages. By using a different business model and cost structure, disruptive entries take hold among the least demanding customers and generally pursue better margins by moving up-market. 

Disruptive Market Entry.pdf

Disruptive Market Entry - thumb.jpeg

A Disruptive Market Entry is one where a firm entering a market chooses not to compete with existing firms for their best customers. Instead, in a disruptive market entry, entrants appeal to customers who have been “overshot” by incumbents.

Disruptive Market Entry

In general, organizations are good at addressing the practical dimension of a solution—avoid fees and get cash back—but fail to consider the social and emotional dimensions. By social, we mean how others see us and by emotional we mean how we feel.

Going back to the milkshake example, the emotional dimensions of a new solution would ideally make a customer feel like they were being healthy and would keep them from sliding into boredom on their drive. The social dimensions would be not coming to work in a dirty car or with messy clothing. Notice also that people bought and consumed a breakfast milkshake alone and were a bit sheepish he when quizzed about their purchase. Although it was as good a solution as they could think of, they didn’t feel like it was healthy and weren’t exactly excited to make that known to others. 

To make a causally effective JTBD, all three hotspots—practical, social, emotional—must work in concert.

Hotspots refers to the three components of a Job to be Done (JTBD). When designing a solution for a JTBD, we should address the practical, social and emotional aspects of that Job. 

In general, organizations are good at addressing the practical dimension of a solution—avoid fees and get cash back—but fail to consider the social and emotional dimensions. By social, we mean how others see us and by emotional we mean how we feel.

Going back to the milkshake example, the emotional dimensions of a new solution would ideally make a customer feel like they were being healthy and would keep them from sliding into boredom on their drive. The social dimensions would be not coming to work in a dirty car or with messy clothing. Notice also that people bought and consumed a breakfast milkshake alone and were a bit sheepish he when quizzed about their purchase. Although it was as good a solution as they could think of, they didn’t feel like it was healthy and weren’t exactly excited to make that known to others. 

To make a causally effective JTBD, all three hotspots—practical, social, emotional—must work in concert.


Hotspots.pdf

Hotspots - thumb.jpeg

Hotspots refers to the three components of a Job to be Done (JTBD). When designing a solution for a JTBD, we should address the practical, social and emotional aspects of that Job. 

Hotspots

Jobs to be Done are not based on customer characteristics and product features. Jobs are discovered by looking at “struggling moments,” times when we’re searching to resolve a problem in our lives, but there isn’t a clear or great solution. 

I often use the example of the energy shot. &nbsp;<a href='/five-hour-energy'>5-hour energy</a> solves a job many of us have. In the mid-afternoon, our circadian rhythm causes a drop in attentiveness. But we’ve still got work to do now—and later when we get home from work. Options range from workout equipment to coffee to Snickers to pillows for a nap. 5-hour energy addresses the job well: keep you productive through the afternoon and early evening, without a sugar or caffeine crash. 

By understanding a place where people struggle, we can better design products and services, so that they fit perfectly in that struggling moment. If we match the solution to the job manifested in the struggling moment, customers will flock to the solution.

The Jobs to be Done lens helps us to see what causes people to bring products/services into their lives. We say that people don’t buy products, they hire them to do jobs for them. The classic way to understand this contrast is that no one goes to the hardware store seeking a 1/4-inch drill bit, they go to the store because they need a ¼"-hole. 

Jobs to be Done are not based on customer characteristics and product features. Jobs are discovered by looking at “struggling moments,” times when we’re searching to resolve a problem in our lives, but there isn’t a clear or great solution. 

I often use the example of the energy shot. &nbsp;<a href='/five-hour-energy'>5-hour energy</a> solves a job many of us have. In the mid-afternoon, our circadian rhythm causes a drop in attentiveness. But we’ve still got work to do now—and later when we get home from work. Options range from workout equipment to coffee to Snickers to pillows for a nap. 5-hour energy addresses the job well: keep you productive through the afternoon and early evening, without a sugar or caffeine crash. 

By understanding a place where people struggle, we can better design products and services, so that they fit perfectly in that struggling moment. If we match the solution to the job manifested in the struggling moment, customers will flock to the solution. 

Jobs to be Done.pdf

Jobs to be Done - thumb.jpeg

The Jobs to be Done lens helps us to see what causes people to bring products/services into their lives. We say that people don’t buy products, they hire them to do jobs for them. The classic way to understand this contrast is that no one goes to the hardware store seeking a 1/4-inch drill bit, they go to the store because they need a ¼"-hole. 

Jobs to be Done

To borrow an example from Malcom Gladwell, consider David and Goliath. Modern readers of the story see it as the victory of the little guy over the big guy. But little guy vs. big guy is the wrong way to understand the story. Goliath was built for conflict at close quarters—his size and strength were advantages, his slowness and poor eyesight were not hindrances. For our purposes, think of him as a grappler with a knife. David was small and there’s no indication he was particularly strong. But he was quick and he was skilled at defending his sheep from predators using a sling. Think of him as a rifleman. It’s pretty clear that a competent shooter will defeat a grappler, so long as the grappler can’t get near. The little guy/big guy categories obscure what David (and Saul) knew—David could reach out and touch the big man. 

Naturally, good categorization is context-sensitive. At close quarters the story of David and Goliath would have been entirely different—big vs. little would’ve been the right categorization. 

Categorization is necessary to connect the messiness of the real world to testable hypotheses. 

*Editor's note: <lens:categorization> does not meet the definition of lens (as of yet) it's foundational so it is included in the lense catalog.*

Categorization is a fundamental component of the scientific method. A key step in building knowledge about the world, is to categorize our observations. As such categorization is essential to understand the phenomena we observe.

To borrow an example from Malcom Gladwell, consider David and Goliath. Modern readers of the story see it as the victory of the little guy over the big guy. But little guy vs. big guy is the wrong way to understand the story. Goliath was built for conflict at close quarters—his size and strength were advantages, his slowness and poor eyesight were not hindrances. For our purposes, think of him as a grappler with a knife. David was small and there’s no indication he was particularly strong. But he was quick and he was skilled at defending his sheep from predators using a sling. Think of him as a rifleman. It’s pretty clear that a competent shooter will defeat a grappler, so long as the grappler can’t get near. The little guy/big guy categories obscure what David (and Saul) knew—David could reach out and touch the big man. 

Naturally, good categorization is context-sensitive. At close quarters the story of David and Goliath would have been entirely different—big vs. little would’ve been the right categorization. 

Categorization is necessary to connect the messiness of the real world to testable hypotheses. 

*Editor's note: <lens:categorization> does not meet the definition of lens (as of yet) it's foundational so it is included in the lense catalog.*

Categorization.pdf

Categorization - thumb.jpeg

Categorization is a fundamental component of the scientific method. A key step in building knowledge about the world, is to categorize our observations. As such categorization is essential to understand the phenomena we observe.

Categorization

Consider the archetypical scientist, Albert Einstein. Without the failure of the Michelson-Morley experiments to measure “ether drift,” Einstein would never have begun to consider the puzzle of why light always moves at a constant speed. 

In my work with Clayton Christensen, we came to recognize that the best path for continued research on Disruptive Innovation was to focus on anomalies. This led to a better understanding of how different circumstances affect the manifestation of disruption. And, more importantly, it helped us to understand where Disruptive Innovation should not be applied. 

When seeking progress in product science, we emphasize not the expected, but the unexpected—anomalies—as the engine for progress.

When testing hypotheses, sometimes we encounter an unpredicted result. Based on years of application and the history of science in the West, we see clearly that science progresses not by way of corroboration—strictly speaking, no hypothesis is confirmed by “successful” experiments—but by way of taking anomalies seriously.

Consider the archetypical scientist, Albert Einstein. Without the failure of the Michelson-Morley experiments to measure “ether drift,” Einstein would never have begun to consider the puzzle of why light always moves at a constant speed. 

In my work with Clayton Christensen, we came to recognize that the best path for continued research on Disruptive Innovation was to focus on anomalies. This led to a better understanding of how different circumstances affect the manifestation of disruption. And, more importantly, it helped us to understand where Disruptive Innovation should not be applied. 

When seeking progress in product science, we emphasize not the expected, but the unexpected—anomalies—as the engine for progress. 


Anomaly.pdf

Anomaly - thumb.jpeg

When testing hypotheses, sometimes we encounter an unpredicted result. Based on years of application and the history of science in the West, we see clearly that science progresses not by way of corroboration—strictly speaking, no hypothesis is confirmed by “successful” experiments—but by way of taking anomalies seriously.

Anomaly

Deliberate strategies are those we’re most familiar with and are aimed at executing what we understand. 

Emergent strategies, on the other hand, are planned, coordinated actions aimed at learning. For example, if we want to create a new product/service or expand the reach of one already in a market, we can design “experiments” to learn what we don’t yet know. 

Sustainable growth requires that we carefully distinguish deliberate and emergent strategies. We have observed the tendency to assimilate all planning to deliberate mode. For instance, I recently interviewed someone working in a long-established firm. They had decided that they needed “an incubator for new ideas.” But I discovered that even those in their “learning group” were designing all of their work as if they already understood the interplay of market, product and people. 

Both kinds of strategy must exist in various places and stages in a long-term successful firm. By distinguishing whether we are planning to execute or planning to learn—and feeding what we learn into a deliberate process—firms can more easily capture growth opportunities, while maintaining health in the core.

Traditional product strategies tend to drive toward deliberate execution. We present another essential kind of strategy: Emergent.

Deliberate strategies are those we’re most familiar with and are aimed at executing what we understand. 

Emergent strategies, on the other hand, are planned, coordinated actions aimed at learning. For example, if we want to create a new product/service or expand the reach of one already in a market, we can design “experiments” to learn what we don’t yet know. 

Sustainable growth requires that we carefully distinguish deliberate and emergent strategies. We have observed the tendency to assimilate all planning to deliberate mode. For instance, I recently interviewed someone working in a long-established firm. They had decided that they needed “an incubator for new ideas.” But I discovered that even those in their “learning group” were designing all of their work as if they already understood the interplay of market, product and people. 

Both kinds of strategy must exist in various places and stages in a long-term successful firm. By distinguishing whether we are planning to execute or planning to learn—and feeding what we learn into a deliberate process—firms can more easily capture growth opportunities, while maintaining health in the core.

Emergent vs. Deliberate Strategy.pdf

Emergent vs. Deliberate Strategy - thumb.jpeg

Traditional product strategies tend to drive toward deliberate execution. We present another essential kind of strategy: Emergent.

Emergent vs. Deliberate Strategy

To know which tool to use—incentives? standard operating procedures? consensus? negotiation?—we begin by framing what’s at issue and assessing the pre-existing degree of agreement on that specific matter. 

For instance, imagine that a software product manager wants to meet a rapidly approaching deadline to ship a product, but the developers see a privacy issue and are pushing back. We frame the issue: the product should be shipped on time. We assess, using a couple of dimensions of pre-existing agreement, to what extent developers and management agree on shipping on time in this particular case. Based on that assessment—and some other simple tips and tricks—the manager can choose the right tool to create a cooperative effort to resolve the issue. 

The Tools of Cooperation have many applications, including how to resolve what seem like intractable disagreements or getting customers to “pull the trigger” and purchase. Crucially, by understanding this framework you can select the right tool for the right job. 

**Example:**
[Tools of Thanksgiving Cooperation](https://www.dx.mba/examples/:thanksgiving-tools-of-cooperation-lens-example)

The Tools of Cooperation provide a way to understand how to get people to voluntarily coordinate their actions in accomplishing desired outcomes. We have seen that some leaders have an intuitive grasp of this, but recognize that choosing the right tool for the right situation can be learned quite easily. 

To know which tool to use—incentives? standard operating procedures? consensus? negotiation?—we begin by framing what’s at issue and assessing the pre-existing degree of agreement on that specific matter. 

For instance, imagine that a software product manager wants to meet a rapidly approaching deadline to ship a product, but the developers see a privacy issue and are pushing back. We frame the issue: the product should be shipped on time. We assess, using a couple of dimensions of pre-existing agreement, to what extent developers and management agree on shipping on time in this particular case. Based on that assessment—and some other simple tips and tricks—the manager can choose the right tool to create a cooperative effort to resolve the issue. 

The Tools of Cooperation have many applications, including how to resolve what seem like intractable disagreements or getting customers to “pull the trigger” and purchase. Crucially, by understanding this framework you can select the right tool for the right job. 

**Example:**
[Tools of Thanksgiving Cooperation](https://www.dx.mba/examples/:thanksgiving-tools-of-cooperation-lens-example)

Tools of Cooperation.pdf

Tools of Cooperation - thumb.jpeg

The Tools of Cooperation provide a way to understand how to get people to voluntarily coordinate their actions in accomplishing desired outcomes. We have seen that some leaders have an intuitive grasp of this, but recognize that choosing the right tool for the right situation can be learned quite easily. 

Tools of Cooperation

Firms often do market research as a way to explore the possibilities for new products and services. But we have found that new-growth opportunities avoid already crowded markets by looking for where people are left out of a market. Successful FinTech firms, for example, have found their entry into the market among the unbanked. In India, Godrej Appliances recognized that only 18% of Indian households had refrigerators. Rather than competing in that small space, they set out to created ChotuKool to reach households that couldn’t afford a traditional refrigerator. 

The core idea of non-consumption is that you can avoid competition—and thus grow your business—by finding opportunities among people or organizations that don’t have access to a solution, or to find a new-growth opportunity in a Job to be Done that lacks a solution altogether.

In looking for disruptive opportunities, it is best to look for what we call “non-consumption.” Non-consumption is manifest by the inability of firms or individuals to obtain a solution. This can be due to the absence of a good solution or to an existing solution’s being inconvenient to acquire and use—including being too expensive. 

Firms often do market research as a way to explore the possibilities for new products and services. But we have found that new-growth opportunities avoid already crowded markets by looking for where people are left out of a market. Successful FinTech firms, for example, have found their entry into the market among the unbanked. In India, Godrej Appliances recognized that only 18% of Indian households had refrigerators. Rather than competing in that small space, they set out to created ChotuKool to reach households that couldn’t afford a traditional refrigerator. 

The core idea of non-consumption is that you can avoid competition—and thus grow your business—by finding opportunities among people or organizations that don’t have access to a solution, or to find a new-growth opportunity in a Job to be Done that lacks a solution altogether. 

Non-Consumption.pdf

Non-Consumption - thumb.jpeg

In looking for disruptive opportunities, it is best to look for what we call “non-consumption.” Non-consumption is manifest by the inability of firms or individuals to obtain a solution. This can be due to the absence of a good solution or to an existing solution’s being inconvenient to acquire and use—including being too expensive. 