Diffusion of innovations

Based on Wikipedia: Diffusion of innovations

Why Some Ideas Catch Fire While Others Fizzle

In 1943, two researchers in Iowa asked a seemingly simple question: why did some farmers plant hybrid corn while their neighbors stubbornly stuck with traditional seeds? The hybrid variety was demonstrably better—higher yields, more disease resistance, greater profits. Yet adoption crept along slowly, taking years to spread through farming communities.

This question—why good ideas take so long to catch on—would eventually reshape how we think about everything from public health campaigns to smartphone adoption to viral marketing.

Nearly two decades later, a rural sociology professor named Everett Rogers pulled together over five hundred studies on this phenomenon. His 1962 book, "Diffusion of Innovations," gave us a vocabulary we still use today: early adopters, tipping points, crossing the chasm. Whether you're launching a startup, promoting a vaccine, or trying to get your organization to embrace new software, you're grappling with the same dynamics those Iowa farmers revealed.

The Five Ingredients of Spread

Rogers identified five elements that determine whether a new idea takes hold or dies on the vine.

First, there's the innovation itself. Not all new things are created equal when it comes to spreading. Second, there are the adopters—the people who might use the innovation. Third, communication channels carry information about the innovation from person to person. Fourth, time matters enormously; diffusion is a process, not an event. And fifth, the social system—the web of relationships, norms, and structures in which potential adopters live—shapes everything.

That last element explains why the same innovation can spread like wildfire in one community and stall completely in another.

What Makes an Innovation Spreadable?

Rogers developed what he called the ACCORD model to describe the characteristics that make some innovations easier to adopt than others. Think of these as the levers you can pull if you're trying to get something to catch on.

Advantage is the most obvious: is this thing actually better than what people are already using? The hybrid corn had a clear advantage—more bushels per acre. But advantage alone isn't enough. Plenty of superior products have failed because they got the other factors wrong.

Compatibility asks how well the new thing fits with existing values, habits, and tools. An innovation that requires people to completely overhaul their routines faces an uphill battle, even if it's objectively superior. This is why electric cars struggled until charging infrastructure made them compatible with how people actually live.

Complexity works in reverse—simpler is better. The easier something is to understand and use, the faster it spreads. Apple built an empire on this insight, making powerful technology feel intuitive.

Observability matters because humans are social creatures who watch what others do. If you can see your neighbor using something successfully, you're more likely to try it yourself. This is why visible innovations—a new style of clothing, a distinctive car—spread faster than invisible ones like household insulation.

Trialability reduces risk. Can you test-drive it before committing? Free trials, sample sizes, and money-back guarantees all leverage this principle. The hybrid corn seeds became more adoptable once farmers could plant them on just a portion of their land to see the results firsthand.

These characteristics don't operate in isolation. An extremely complex innovation might still spread if its advantage is overwhelming enough. But generally, innovations that score well on all five dimensions diffuse much faster than those that don't.

The Adoption Curve: A Portrait of Human Nature

Perhaps Rogers' most famous contribution is the bell curve of adopter categories. Plot adoption over time, and you get an S-curve. But slice that curve into segments, and you reveal something profound about human nature.

At the front edge are the innovators—the true pioneers. They represent roughly two and a half percent of any population. These are the people who try new things for the sheer joy of trying new things. They can tolerate a high degree of uncertainty and occasional failure. They're often seen as eccentric by their peers.

Next come the early adopters, about thirteen and a half percent of the population. These aren't just technology enthusiasts—they're opinion leaders. Others watch them closely. When early adopters embrace something, it gains legitimacy. They're respected enough to influence the mainstream but adventurous enough to take calculated risks.

The early majority represents about thirty-four percent. These are thoughtful people who adopt new ideas before the average person but only after they've seen evidence that the innovation works. They're deliberate, rarely leaders, but important in legitimizing an innovation.

The late majority, another thirty-four percent, approaches innovation with skepticism. They adopt primarily because of economic necessity or increasing social pressure. By the time they come around, most of their community has already embraced the new thing.

Finally, the laggards—about sixteen percent—are the last to adopt. Traditional in their outlook, they're often suspicious of change and change agents. By the time laggards adopt an innovation, it may already be superseded by something newer.

The Chasm That Kills Good Ideas

In 1989, consultants at a firm called Regis McKenna noticed something troubling in that adoption curve. Between the early adopters and the early majority lay a gap—a discontinuity that many innovations never successfully crossed.

They called it "the marketing chasm."

Here's the problem: early adopters and the early majority want fundamentally different things. Early adopters are drawn to novelty and can tolerate rough edges. They'll figure out workarounds for bugs and missing features. The early majority wants solutions that just work. They want references from people like themselves, not from the adventurous early adopter crowd.

Many promising innovations gather enthusiastic early adopters and then... stall. The strategies that attracted the pioneers don't work on the pragmatists. This is where critical mass becomes crucial—the point at which enough people have adopted that the innovation becomes self-sustaining. Cross the chasm, and you hit escape velocity. Fail to cross it, and your innovation remains a niche curiosity.

Why People Say Yes

Understanding individual adoption decisions means understanding human psychology. Two factors matter most: ability and motivation.

Ability is straightforward—can this person actually use the innovation? Do they have the skills, resources, and knowledge required? A brilliant app is useless to someone without a smartphone.

Motivation is more complex. Innovations carry symbolic meaning. Driving a Tesla says something about who you are (or who you want to be). Using a flip phone in 2024 makes a statement too. People adopt innovations partly for their functional benefits and partly for what adoption means about them.

Connection to broader networks matters as well. Researchers found that farmers who frequently visited cities were more likely to adopt innovations. Exposure to diverse ideas and practices opens minds to new possibilities.

Power and agency play roles too. People who can actually implement changes in their lives are more likely to adopt innovations than those constrained by circumstances beyond their control. This helps explain why organizational change is so difficult—most employees lack the authority to adopt innovations unilaterally, even beneficial ones.

When Organizations Adopt

Organizations are more than just collections of individuals. They have their own procedures, norms, cultures, and inertias. This makes organizational adoption both similar to and different from individual adoption.

Three forces drive organizational adoption. First, tension for change—is the organization's current situation untenable? Crisis creates openness to new approaches that comfortable stability never would. Second, innovation-system fit—how well does the new thing mesh with existing processes, technologies, and ways of working? Third, assessment of implications—can decision-makers clearly see what the innovation will do?

External pressure matters enormously. When competitors adopt something, organizations feel compelled to follow. When regulations mandate change, adoption accelerates. When an innovation sweeps through an industry, even skeptical organizations eventually climb aboard.

Interestingly, researchers have found that simple behavioral models can predict organizational technology adoption fairly well—but only when proper screening procedures filter out the obviously inappropriate options first. Organizations that carefully evaluate their needs before considering specific solutions make better adoption decisions.

The Five Stages of Adoption

Adoption isn't a moment—it's a journey through five distinct stages.

In the knowledge stage, a person first becomes aware that the innovation exists. They don't yet know much about it, just that it's out there. In the persuasion stage, they form an attitude—favorable or unfavorable—toward the innovation. They start actively seeking information.

The decision stage involves committing to either adopt or reject. This often involves some trial use. In the implementation stage, the person actually starts using the innovation, which often requires behavioral changes. Finally, confirmation is when the adopter seeks reinforcement for their decision—and may still reverse course if they encounter contradictory information.

People can reject an innovation at any point in this process. They might reject it early, never getting past initial awareness. They might try it and abandon it during implementation. They might even reject it after years of use if something better comes along.

When Diffusion Fails

Not every innovation reaches widespread adoption. Failed diffusion is common, and understanding why helps explain what makes successful diffusion so remarkable.

Sometimes innovations fail on their own merits—they're simply not good enough. Sometimes they lose to competition from rival innovations. Sometimes awareness never spreads widely enough for the innovation to get a fair hearing.

From a network perspective, failed innovations often spread within tight-knit clusters but never bridge to other communities. Picture a brilliant tool that every developer at one company uses, but that never escapes to the broader market. The connections that would carry it outward simply don't exist—or are too weak to carry the message.

Paradoxically, networks can also be too connected. When everyone knows everyone, and established practices are deeply embedded, the rigidity that results can prevent the changes an innovation would require. Disruption needs some looseness in the social fabric.

The Cautionary Tale of Los Molinos

Rogers documented a particularly instructive failure in a Peruvian village called Los Molinos. Public health workers spent two years trying to convince residents to boil their drinking water—a simple practice that could prevent serious illness.

The campaign mostly failed. Why?

The villagers had no concept of germs or the link between water purity and health. More importantly, local culture associated boiled water with sickness—it was something you gave to people who were already unwell. Healthy people drinking boiled water was not just unnecessary but mildly shameful.

The innovation (boiling water) had high relative advantage (preventing disease) and low complexity (anyone could do it). But it scored terribly on compatibility with existing beliefs and practices. The campaign ignored the local meaning-making that shaped how villagers understood their world.

Research in El Salvador later revealed that communities often have multiple overlapping networks—one that carries information and another that carries influence. Villagers in Los Molinos might have received information about boiling water, but the influence network—the web of status and social pressure that shapes behavior—pushed against adoption.

Birds of a Feather: Homophily and Heterophily

Sociologists Paul Lazarsfeld and Robert Merton coined the term "homophily" to describe our tendency to associate with similar people. We gravitate toward those who share our education, values, social status, and beliefs. It's comfortable. Communication flows easily when you share assumptions and vocabulary.

This has implications for diffusion. Homophilous communication—between similar people—is more effective at changing attitudes and behaviors. When someone like you recommends something, you're inclined to listen.

But homophily creates a problem. If we only talk to people like ourselves, how do new ideas ever enter our social circles? This is where heterophily—connection across difference—becomes essential. Diffusion requires at least some bridging ties that span social boundaries.

The ideal situation for diffusion involves people who are similar in most ways but different in one crucial respect: one of them knows about an innovation the other hasn't encountered yet. They're similar enough for effective communication but different enough to have something new to share.

This explains why weak ties—acquaintances rather than close friends—often matter more for spreading innovations. Your close friends tend to know what you know. Your acquaintances move in different circles and can bridge information gaps that would otherwise keep innovations contained.

The Origins of a Theory

The study of how things spread has deeper roots than Rogers' 1962 book. French sociologist Gabriel Tarde explored diffusion in the late nineteenth century. German and Austrian anthropologists and geographers like Friedrich Ratzel and Leo Frobenius studied how cultural practices and technologies spread between societies.

The modern form of diffusion research emerged in the American Midwest during the 1920s and 1930s. Agricultural technology was advancing rapidly—new seeds, new equipment, new techniques—and researchers wanted to understand why some farmers adopted innovations quickly while others lagged behind for years or decades.

The 1943 study of hybrid corn adoption by Ryan and Gross crystallized this work into a coherent paradigm. They established adoption as a process rather than a moment, identified different categories of adopters, and documented the social dynamics that shaped diffusion patterns.

Since then, diffusion theory has spread far beyond agriculture. It's been applied to medical practices, educational reforms, marketing campaigns, development programs, organizational change, conservation efforts, and countless other domains. The core insights—that adoption is a process influenced by innovation characteristics, adopter traits, communication patterns, social structures, and time—have proven remarkably durable and widely applicable.

What This Means Today

In the age of artificial intelligence, social media, and rapid technological change, diffusion dynamics matter more than ever. New tools and practices emerge constantly, and the organizations and individuals who adopt wisely gain tremendous advantages.

Understanding diffusion helps explain why some AI tools are spreading rapidly while others languish. Why some remote work practices stuck after the pandemic while others faded. Why some companies embrace new technologies quickly while others resist for years.

The insights from those Iowa cornfields nearly a century ago still apply. Innovations spread through social networks via communication channels over time. Different people adopt at different speeds for different reasons. And the gap between early enthusiasm and mainstream adoption—that marketing chasm—remains the graveyard where countless promising innovations go to die.

The theory of diffusion doesn't tell us which innovations deserve to succeed. But it helps us understand why some do and some don't—and perhaps how to tilt the odds.