You didn’t plan to get a smartwatch—your phone already does the job. But then your coworkers start talking about sleep score. Your cousin checks their heart rate during a movie. A friend taps their wrist to pay for groceries. These moments feel ordinary, until they start stacking up. You’re still on the fence, but suddenly the product is everywhere—on wrists, in ads, in casual conversations. What once seemed like an optional upgrade now feels stitched into daily life. At a certain point, the question you ask yourself flips. You’re no longer asking whether the product is useful. You’re asking whether it feels normal to go without it.

That shift is what diffusion of innovation helps explain. The theory outlines how new ideas, behaviors, or products spread through a population—not all at once, but across five stages.¹ First are the innovators, the adventurous few who try something simply because it’s new. They’re followed by early adopters, who often care about social identity, reputation, or being ahead of the curve. Then comes the early majority, a more deliberate group that opens up to the idea once it proves useful. The late majority waits until the innovation blends into everyday life, when using it feels expected rather than experimental. And finally, the laggards, who adopt last—often out of necessity rather than choice.

What makes an invention or idea move through these stages? According to the diffusion of innovation theory, five characteristics shape how quickly (or whether) an innovation will spread.

• Compatibility: Does it fit into things people already do?
• Relative advantage: Does it seem better than the alternative?
• Observability: Can others see the benefits in action?
• Simplicity: Is it easy to understand and use?
• Trialability: Can people try it without a big commitment?

The more of these boxes the concept checks, the smoother the journey from fringe idea to widespread norm.

Rather than simply how an innovation works, a major tipping point is how many people seem to be using it.  We often look to others for cues on what deserves our time. When an innovation appears in daily life, whether at the gym, during meetings, or across social media, it gradually starts to feel familiar.² And with familiarity comes legitimacy. You don’t need a full explanation to believe it works. You just need to see enough people using it. 

To sum up, diffusion of innovation doesn’t predict which ideas will thrive. However, it does give us a lens to understand how change takes shape, and why some innovations gain traction while others fade. It also leaves us with one last reminder: everything that seems ubiquitous today once belonged to a single early innovator who took the first risk.

Adoption Curve: The progression by which new ideas or technologies spread through a population over time. It begins with innovators and early adopters, rises as the early and late majority join in, and eventually levels off with the final group called laggards, who adopt much later, often out of necessity rather than enthusiasm.¹

Compatibility: The degree to which a new idea fits into existing habits, systems, or values. The more seamlessly something aligns with what people already do, the easier it is to adopt.

Relative Advantage: The degree to which an innovation is perceived as better than what it replaces. If a new product offers clear benefits, whether in cost, convenience, or outcome, it’s more likely to spread.

Observability: How visible the benefits of an innovation are to other potential users. The easier it is to see someone use a product successfully, the more likely others are to follow. 

Simplicity: The extent to which a new idea or product is easy to grasp and use. When something feels uncomplicated or familiar, people are more likely to adopt it without hesitation. 

Trialability: The extent to which people can experiment with a new product or idea before committing. When trying something new feels low-risk or reversible, people are more open to adoption.

Innovators: The first individuals to adopt a new idea or product, according to the five-stage model of diffusion of innovation. Often risk-takers or tech enthusiasts, they are motivated by novelty and a desire to experiment early.

Early Adopters: Individuals who adopt a new idea or product soon after it’s introduced. They’re often influential within their social groups and play a critical role in spreading innovations beyond niche users.

Laggards: The final group to adopt a new product or idea. Often skeptical or cautious, they typically adopt only when the innovation has become unavoidable or deeply embedded in their environment.

Individual-Blame Bias: A tendency to assume that when someone resists adopting a new idea, the fault lies with the individual rather than the structural or systemic barriers that might be shaping their decision.⁴ This bias often overlooks environmental and institutional factors and leads to oversimplified conclusions about why change does or does not take place.

Long before theories formalized it, innovation traveled through social interactions. Agricultural knowledge moved through trial, imitation, and survival. Corn cultivation, for example, began in Mesoamerica over 7,000 years ago and slowly traveled across continents, eventually reaching Europe after contact between Indigenous communities and European explorers.⁵ During the Renaissance, fashion, philosophies, and printing methods crisscrossed social classes and trade routes.⁶ The process wasn’t always recorded, but it was already in motion.

The first formal attempt to explain the diffusion of innovation came in 1903. French sociologist Gabriel Tarde proposed that new ideas followed a recognizable pattern.⁷ He noticed that innovations started with a small group, gained traction gradually, and eventually surged in popularity once social exposure reached a critical mass. To map this pattern, he drew an S-shaped curve. Slow beginnings. A sharp rise. Then a plateau as saturation sets in. For Tarde, imitation was more than mimicry. It was a mechanism for cultural evolution.

Forty years later, the idea gained further empirical weight. Agricultural researchers Bryce Ryan and Neal Gross wanted to understand how hybrid corn spread among Iowa farmers.⁸ It turned out that despite clear advantages, including stronger crops and better yields, not everyone rushed to adopt this approach. Some farmers were eager to experiment. Others waited years. Ryan and Gross documented these patterns and introduced what would become the five adopter categories: innovators, early adopters, early majority, late majority, and laggards.

Two decades later, American sociologist Everett Rogers tied these findings together in his 1962 book Diffusion of Innovations.⁹ In it, Rogers offered a clear definition: diffusion is the process by which an innovation is communicated through specific channels over time among members of a social system. Innovations don’t spread in isolation. They move through conversation, observation, and relationships. They gain ground not when people are convinced by a single argument, but when enough small signals accumulate to tip the balance. 

Rogers also outlined a five-stage process describing how individuals adopt something new:

1. Awareness: You’re exposed to a new idea, but the details are vague. You know it exists, but not much more.
2. Interest: Curiosity kicks in. You start asking questions or casually researching.
3. Evaluation: You consider the idea in context, including how it might fit your needs or solve a particular problem.
4. Trial: You test it out, either through a free trial, a small purchase, or by observing someone else’s experience closely.
5. Adoption: You commit. The innovation becomes part of your routine, possibly replacing whatever came before.

Since then, the theory has been used to explain everything from the rise of smartphones to creating more effective public health messaging. The rollout of the internet in the 1990s followed the expected pattern.¹⁰ First came the innovators and tech enthusiasts. Then early adopters—students, entrepreneurs, gamers. As interfaces became friendlier and broadband more accessible, the early majority came on board. By the 2000s, internet access became a household norm. Even laggards such as older adults or those skeptical of technology eventually signed on, sometimes just to email grandkids or book medical appointments.

Today, healthcare offers a striking lens on how innovation spreads. Vaccination campaigns, like those for HPV or COVID-19, demonstrate how visibility, trust, and timing shape adoption. Early adopters often cite civic duty or science literacy.¹¹ The early and late majority typically wait for reassurance from peers or physicians. And laggards may resist entirely unless policies, mandates, or shifting norms compel them.¹² Diffusion of innovation doesn’t guarantee that something will succeed. However, it does help clarify why some ideas take off while others stall. It reminds us that adoption isn’t just about function or design. It’s about seeing, hearing, and repeating—until the unfamiliar becomes familiar and opting out feels stranger than opting in.

Gabriel Tarde

A 19th-century French sociologist and criminologist, Tarde was among the first to describe how ideas spread through society.⁷ He argued that innovations begin in small groups and gain momentum through imitation. In 1903, Gabriel Tarde proposed that innovation spreads through imitation and follows a recognizable pattern—a concept that later inspired the S-curve popularized by Rogers. 

Bryce Ryan & Neal C. Gross

In the 1940s, sociologist Bryce Ryan and graduate student Neal Gross at Iowa State University conducted the first empirical study of how innovation spreads.⁸ By tracking how hybrid corn was adopted across Iowa farming communities, they identified consistent patterns in who adopted first and who waited. Their research introduced five adopter categories—innovators, early adopters, early majority, late majority, and laggards—all of which remain central to the diffusion of innovation theory today.

Everett Rogers

A communication scholar and rural sociologist, Everett Rogers synthesized decades of research in his 1962 book Diffusion of Innovations.⁹ He defined diffusion as the process through which an innovation spreads via specific channels over time among members of a social system. His work transformed a loosely observed trend into a structured theory that continues to be used in marketing, healthcare, education, and tech.

From AI in the workplace to vaccine campaigns and digital health tools, the success of innovation rarely hinges on design alone. Diffusion of innovation helps explain how new ideas gain ground or lose momentum by tracing who adopts them, when they do so, and under what conditions.

The spread of AI at work

Artificial intelligence is transforming the workplace, but its spread has been neither smooth nor universal.¹³ While some businesses and teams adopt it eagerly, others hold back—not necessarily because the technology is flawed, but because its purpose or value remains unclear. In a survey conducted by the communication platform Slack, nearly 50% of employees reported feeling embarrassed using AI at work.¹⁴ Some feared it made them appear lazy. Others worried it signaled they were replaceable.

This discomfort reflects a broader truth: AI adoption doesn’t follow a straight line. It follows the curve described by diffusion of innovation theory, where new technologies spread gradually, first through risk-takers, then early adopters, followed by a slower-moving majority, and finally, those who adopt out of necessity.

A recent longitudinal study tested this framework more directly, measuring employee attitudes toward AI across three timepoints using five core traits from diffusion theory: relative advantage, compatibility, observability, trialability, and simplicity.¹⁵

Three traits stood out. AI was better received when it seemed genuinely helpful (advantage), when it fit how someone already worked (compatibility), and when its benefits were visible in real settings (observability). These factors consistently predicted more positive attitudes over time. But trialability, the idea that people need chances to explore a new tool, didn’t seem to move the needle. Participants were asked whether they had opportunities to try AI at work and whether they knew where to experiment with it. Over time, these ratings barely changed, and they didn’t predict shifts in attitude. Testing it wasn’t enough. Seeing it in action and knowing it worked for someone else was more persuasive.

It didn’t matter how simple the interface was. What mattered was what the technology signaled in terms of advantage: Am I actually benefiting from this? Will it lighten my workload? On the flip side, the perception that AI could create disadvantage predicted more negative attitudes. Employees who feared being replaced by AI grew increasingly wary, even when the tool improved productivity. Subtle concerns about status, relevance, or being seen as expendable were enough to undermine adoption.

Ultimately, AI’s diffusion into the workplace isn’t driven by its tech features alone. It hinges on the surrounding culture. People don’t just ask, “Does this work?” They ask, “Is it safe to use?” If AI appears useful, visible, and aligned with how someone sees themselves at work, it spreads. But when it’s wrapped in fear or stigma, even the most advanced tool struggles to take hold. 

Healthcare adoption and trust

One of the most enduring insights from diffusion of innovation theory is that trust fuels momentum.^1,9 New ideas don’t spread just because they’re available. They take off when people believe the messenger. And when trust falters, adoption slows—no matter how useful the product or promising the concept might be.

Recent research makes that clear. A 2023 survey by Matter Communications, a major public relations agency, found that 81% of consumers had researched, considered, or bought something after seeing a friend, family member, or influencer post about it.¹⁶ But it wasn’t fame that moved them. Just 11% had preferred celebrity endorsements. Far more gravitated toward buying something from those who felt real, like relatable personalities with everyday experiences, practical insights, or niche expertise. These voices aren’t flashy. They’re familiar. And that makes all the difference. 

Healthcare decisions offer a sharper test. Vaccine uptake, for instance, often hinges on who’s doing the recommending. In 2019, the World Health Organization named vaccine hesitancy one of the top ten threats to global health.¹⁷ Since then, from measles to COVID-19, we've seen how trust—or the lack of it—can shape entire public health outcomes.¹⁸ Patients frequently report trusting a healthcare provider who speaks their language, understands their background, or looks like them.¹⁹ Health professionals who reflect these qualities are far more likely to be believed. That credibility isn’t just a bonus. It’s one of several factors that can influence someone’s decision to roll up their sleeve.²⁰ 

The influence of laggards

One lasting contribution of diffusion of innovation theory across industries and disciplines is its ability to map how adoption unfolds. Rather than lumping users into one amorphous group, it charts a curve that captures the spread of new ideas over time among five groups. Most research has focused on the front of the curve; those eager to try something new tend to attract the most attention.²¹ What often gets missed is the value of those at the back.

Laggards, the final group in the diffusion of innovation model,  are often portrayed as technophobic, resistant, or irrelevant.²¹ However, that framing oversimplifies a more complex reality. In a narrative review, Jahanmir and Lages argue that laggards aren’t irrational with their decision to opt out of a product or new technology: they’re discerning.²¹ They may hesitate for good reason: high costs, lack of access, unfamiliar interfaces, or structural barriers that early adopters never had to face. Once those issues are addressed, something interesting happens. These same users often become more loyal to the product than the early crowd ever was. One study even found that late adopters were more likely to remain committed in the long term compared to those who jumped in early.²²

And when laggards do share their experiences, they seem to hit differently.²¹ They appear to be especially persuasive to the people who are still on the fence. In fields such as health tech, education, and public service, where uneven adoption can deepen inequality, this kind of credibility becomes invaluable.

From a design standpoint, laggards are sharp-eyed critics. They’re often the ones pointing out usability flaws, inaccessible features, or onboarding experiences that feel confusing or exclusive. These critiques don’t come from a place of rejection. They often reflect unmet expectations. Listening to those concerns doesn’t just increase reach, but also elevates the product’s utility for everyone.

And perhaps most importantly, laggards aren’t rare. They make up nearly one in five people. That’s not a fringe audience. That’s a test case for whether something truly works across contexts. If a product doesn’t work for 20% of the population, can we really call it innovative?

While diffusion of innovation theory has shaped how we understand change, it’s far from immune to critique. From structural blind spots to overly linear assumptions, the model can sometimes flatten the complexity of how and why people adopt new ideas.

Adoption fails when systems don’t work

A persistent critique of diffusion of innovation theory is what Everett Rogers called the individual-blame bias.⁹ This bias describes the tendency to assume that if someone resists adopting a new idea, the problem lies with them, and not with the broader system shaping that decision. The theory often centers around the perspective of the change agent: adoption is treated as the goal, and those who lag behind are seen as misinformed, risk-averse, or simply hard to reach. As Rogers once put it, “If the shoe doesn’t fit, there’s something wrong with your foot.”⁹

In many cases, however, the barriers to adoption aren’t personal. They’re structural.

The case of Ghana offers a compelling example. In low-income countries, technological innovation isn’t a luxury—it’s generally a critical path toward industrial development and economic stability.²³ Yet the conditions needed to support innovation don’t always exist. From 2011 to 2013, the Determinants of Innovation in Low-Income Countries (DILIC) project led by researchers at Oxford University surveyed 500 firms across all ten regions of Ghana.²⁴ Their aim was to capture how innovation happens—and why it sometimes doesn’t—within both formal and informal sectors of industry.

Importantly, the study didn’t define innovation in narrow terms. It covered a range of meaningful change: improved products, new technologies like mobile phones or industrial equipment, total factor productivity gains, and even the adoption of new management or marketing practices. By these standards, nearly 80% of the firms surveyed had introduced some form of innovation during the three-year period. 

Still, most firms reported facing steep, persistent barriers. Two in particular stood out: a chronic lack of capital and limited access to external financing. Innovation was very expensive. Many firms wanted to try something new but couldn’t afford the risk. On top of that, technical support and skill-building resources were in short supply. Diffusion channels were unclear or entirely missing. Even in firms where the will to innovate was strong, the path forward remained blocked. 

This is where diffusion theory starts to fall short. It asks: “Why haven’t they adopted?” But in Ghana’s case, the better question is: “How are they managing to innovate despite these significant barriers?” This shift in framing matters a lot. Instead of asking why someone hasn’t adopted a new idea, maybe we should ask what systems they’re operating within, and whether those systems make innovation feel possible. Diffusion theory still offers value, but only if it expands to include the structural conditions shaping the behaviors we aim to explain.

Relative advantage: a double-edged trait?

According to diffusion of innovation theory, relative advantage is one of the five core traits that help explain why some innovations take off while others don’t. If something appears more efficient, more affordable, or more rewarding than what came before, it tends to gain traction. But that raises an important question: better for whom, and at what cost?

ChatGPT offers a timely case study. For students, it checks all the right boxes: free, fast, easy to access. A recent experimental study found that undergraduates who used ChatGPT as a study aid reported higher cognitive engagement than those who used traditional study techniques.²⁵ But the story doesn’t end there. That same group that relied on AI also showed significantly more procrastination and, in the end, performed worse on a post-test quiz. The tool made studying feel smoother, but it may have dulled opportunities for deeper thinking, retention, and reflection along the way. 

Another narrative review has echoed this caution, pointing to growing concerns around overreliance on AI and reduced critical thinking.²⁶ If an innovation streamlines effort but undercuts mental engagement, is that truly an advantage? Or is it simply convenience dressed up as progress? 

And that’s just the individual level. Behind the scenes, there’s another kind of price. Large AI models like ChatGPT demand enormous amounts of electricity and fresh water to run.²⁷ Most of this resource use isn’t visible to end users, but it matters. By some estimates, AI’s global water footprint could exceed 6.6 billion cubic meters by 2027.²⁸ And as power grids strain under the weight of population growth and climate change, the energy demands of data centers will likely compound existing vulnerabilities.

Diffusion theory treats relative advantage as a key predictor of adoption, but leaves its definition remarkably open-ended. The result is a characteristic that can easily be oversimplified. What counts as “better” depends on who’s asking, what they need, and which trade-offs they’re willing to accept. In practice, those trade-offs can be cognitive, ethical, environmental, or all three at once.

Adoption isn’t always linear

Diffusion theory tends to chart innovation as a smooth, steady climb. First come the trailblazers. Then, the early adopters. The early and late majority follow, and eventually the laggards catch up.⁹ Plot that path on a graph and you get something elegant: an S-curve that seems to promise inevitability.

But in real life? Adoption often refuses to play by that script. The path is rarely that tidy. Momentum can build fast, stall unexpectedly, or unravel altogether. Media coverage, economic pressures, and policy shifts all shape momentum. A wave of hype might ignite mass interest overnight. One skeptical headline, a regulatory shift, or market correction can bring the climb to a halt.

NFTs (non-fungible tokens) illustrate this break in the S-shaped curve. In 2021, they dominated the internet. Digital images sold for millions.²⁹ Musicians, athletes, and influencers cashed in on exclusive drops. To own one was to signal that you were early: part of something experimental, edgy, and ahead of the curve.

For a moment, it seemed like NFTs were gliding smoothly up the adoption curve. 

Then came the reversal. Public discourse turned sour. Reports of scams, market speculation, and environmental harm began to dominate the headlines. Confidence dropped. Trading volume collapsed. By 2023, once-hyped collections had lost nearly all their value. According to blockchain analyses, NFT transactions fell by over 90% from their peak, and tens of thousands of tokens had been abandoned.²⁹ 

This wasn’t a slow fade. It was a free fall.

So, while diffusion theory gives us a helpful framework, it’s worth remembering: not all innovations glide upward. Some surge and sink, others take detours, and a few will burst onto the scene, only to vanish just as fast.

Glass promised a future few embraced

Google Glass was supposed to change everything. In 2014, Google introduced a wearable device that promised a seamless blend of digital and physical life.³⁰ With a tilt of the head or a voice command, users could send messages, check the weather, or take photos—no phone required. But despite the hype, Glass became a cautionary tale of how innovations fail to diffuse.

According to diffusion of innovation theory, new products gain traction when they hit five key notes: relative advantage, compatibility, simplicity, trialability, and observability. Glass flubbed nearly all of them.³¹ 

First, it didn’t outperform existing tools. Its relative advantage was murky: why spend $1,500 on a face computer when your smartphone already did the job? Its compatibility with everyday life was even worse. Early users looked conspicuously robotic, which clashed with social norms and fashion standards at the time.^30,31 Even Glass’s trialability and observability, two traits that usually help new tech spread, worked against it. You couldn’t casually try it out at a store or borrow one from a friend. And when you did see it out in public, it wasn’t the benefits that stood out. It was the awkwardness.

A netnographic analysis of Reddit discussions among users and those interested in the product uncovered an undercurrent of discomfort and self-consciousness among early adopters. One user wrote, “It’s unpleasant to look around. People avoid me, because they think I’m a constantly recording robot… No one looks me in the eye.” Another shared, “I’ll probably spend a year or two using Glass just in the car and around the house… because I would not be good at overcoming embarrassing situations.”³¹

Design tweaks also didn’t help much. Although Google released new frames in hopes of overcoming fashion pushback, users still described it as looking like a “cybernetic tape” or “metal rod glued to my face.”³¹ One person admitted they’d only wear it to football games or concerts, where the novelty might be tolerated.

In the end, Glass didn’t fail because it lacked technical power. It failed because it didn’t land socially. It asked people to change how they looked, how they interacted, and even how they were perceived. Most people weren’t ready to make that trade. For new technology to succeed, it has to feel like it fits your habits, your norms, your sense of self. Without that, even the most revolutionary tool starts to feel out of place.

Microwaves weren’t always so hot

Diffusion doesn’t always mean overnight success. Sometimes, it takes decades and a shift in culture for a product to finally catch fire. The microwave oven is a perfect example.

When it first emerged in the 1940s, the technology was revolutionary, but wildly impractical for the average household.³² The earliest models from Raytheon were over five feet tall, weighed more than 750 pounds, and cost around $5,000 (close to $50,000 today). On top of that, they needed custom plumbing to keep the magnetron cool. In 1955, Raytheon released a smaller version for home use, but it was still clunky and expensive, priced at $1,300.

The real breakthrough came in 1964, when Japanese engineers developed a compact, energy-efficient electron tube.³³ This made it possible to produce countertop models that were smaller, sleeker, and far more affordable. One could reheat yesterday’s leftovers in 45 seconds. Still, even with improvements in cost and design, the microwave didn’t catch on right away. It was fast, sure. But it was also unfamiliar. Cooking with invisible waves of radiation didn’t exactly scream “safe” to the average household.

Then came a shift—not in technology, but in society. During the 1970s and early ’80s, more women entered the workforce.³⁴ In Canada, for instance, the number of married women employed outside the home jumped from less than 25% in the 1950s to over 70% by 1990.³⁵ This growing shift meant less time for cooking, and instead, a growing appetite for speed and convenience in the kitchen. Suddenly, microwaves weren’t just gadgets. They were solutions.

That’s when diffusion finally took off. The appeal of speed, the fit with shifting household roles, and the growing visibility of microwaves in commercials, magazine spreads, and neighborhood kitchens all started to align. People could try them, see them in action, and gradually trust them. By the mid-1980s, the microwave had gone from fringe appliance to a kitchen staple.

While the microwave’s early rollout was rocky, it eventually earned its place by syncing technical progress with social change. Once all five elements of diffusion theory lined up, the appliance spread widely. And today, over 90% of American homes have one.

Innovation, Explained

It’s clear that some ideas take off, while others fall flat. In this TDL reference guide, our writer Kira Warje explores how new ideas, products, services, and solutions emerge to rethink traditional approaches and deliver meaningful, tangible results. From workplace systems to business models and tech design, she examines the conditions that help new ideas gain traction and thrive.

Social Physics, Explained

Ideas don’t spread in isolation. Social physics helps us understand how behaviors and beliefs move through networks, shaping collective action along the way. Much like diffusion of innovation, this framework looks at how people influence one another, not through the products alone, but through the patterns of interaction. This reference guide explores those dynamics across public policy, urban planning, idea sharing, and other domains where influence travels person to person.

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