A Macro view

New ideas spread progressively: awareness travels along communication channels until it permeates a social network — or the slice of that network you hope to convert, i.e., your target market.

We can frame diffusion by blending three perspectives: Bass’s division into innovators and imitators; Rogers’s five adopter groups — innovators, early adopters, early majority, late majority, and laggards; and Moore’s notion of “crossing the chasm.”

(IMAGEM)

Each adopter segment tends to respond to different cues than the segment immediately before it. That means our diffusion strategy must evolve at every hand-off. Maloney’s 16 percent rule says the first big pivot comes once roughly one-sixth of the population has taken up the idea— right where Moore’s “chasm” begins. The pioneering 16 percent are motivated by scarcity and exclusivity, whereas the groups that follow look for evidence that “people like me” already use the product.

Moore argues that bridging the chasm requires anchoring in one tightly defined niche and then expanding to neighboring niches, step by step. Gladwell’s trio of connectors, mavens and salespeople reminds us that adoption spreads along social ties. So the real questions are: can we pinpoint and activate the key influencers, and can we engineer conditions for the idea to propagate organically—perhaps even go viral?

 
Implications

Grasping the mechanics of diffusion lets us design tactics that accelerate visibility for new ideas. Identifying and activating key influencers is central to that task. We need an enticing offer for the innovator segment first, then a revised narrative and channel mix for the imitators who follow. In tech markets especially, progress can falter at a well-defined “chasm,” so our plan must include a clear bridge that keeps adoption moving beyond that point.

The Concept

You’ve successfully transformed a raw concept into a bona-fide innovation—one that helps people make meaningful progress in some part of their lives faster or better than current options (see the evolved definition of innovation). You’ve tackled the factors that accelerate adoption and trimmed down sources of resistance.

What’s left is spreading the word so potential beneficiaries actually discover it and begin to use it.

That visibility grows as the innovation travels through a social network: knowledge jumps from person to person. Transmission can be active—via word-of-mouth, advertising, social-media influencers, and so on—or passive, as when someone simply notices others using the product and gets curious.

This article looks at the foundations of diffusion, taking a network-centric view. Feel free to dig deeper into how network topology (its shape and traits) shapes diffusion, or to geek out on the math behind the process.

To ground the discussion, we’ll begin by revisiting the textbook definition of diffusion.

A traditional take on diffusion

Everett Rogers, in his landmark book The Diffusion of Innovations, describes diffusion as the way a new idea moves through specific communication channels, over time, within a particular social group.

Seen from a different angle, your target audience could be the entire social network—or merely a chosen slice of it.

The social system through a network lens

Put simply, a social system is a group of individuals or organizations that stay in touch—nodes joined by ongoing communication, as shown in Figure 1.

(FIGURA 1)

Individuals or organisations form the nodes, and the relationships between them act as the communication channels.

Once news of your innovation enters this web, it travels along those channels through conversations, reviews, conference exchanges, and even as people shift between companies, markets, or industries.

Every network has recognisable traits. Some nodes are sparsely linked, so diffusion can fizzle out when it reaches them. Others are highly connected and function as hubs or influencers. Figure 2 illustrates several common network structures; consider how each one might speed up or slow down the spread of your idea.

(figura 2)

Grasping the layout of your social network lets you pinpoint the best entry points for sharing your innovation and foresee potential bottlenecks in its spread. I examine these issues in more detail in another article.

Connection to Adoption

The spread of an innovation is intrinsically linked to how and whether it gets adopted.

“Adoption – getting a new idea adopted, even when is has obvious advantages, is difficult”

Rogers (2003)

The two phenomena are almost inseparable. When an innovation isn’t taken up, its spread quickly stalls—there’s nothing for others to observe, use, or review.

Diffusion through a social network also follows a surprisingly consistent pattern: a minority of members behave as pioneering innovators, while the majority watch and imitate their lead.

(figura 3)

Innovators & Imitators: The Engines of Innovation Diffusion

During the 1960s, Bass recognised that participants in a diffusion network fall into two broad camps. A small group the innovators actively hunts for and quickly adopts new ideas, whereas the rest the imitators take a wait-and-see approach and follow once they see others succeed. In Figure 3, the network is depicted as two distinct segments that reflect this split.

(IMAGEM 3)

A quick note up front: in this and the following diagrams I simplify things by letting time—and the spread of the idea—run from left to right. Real‐world networks are messier, but this layout keeps the explanation clear.

In the 1960s, Bass introduced a mathematical diffusion model that predicts how adoption unfolds. The equation neatly captures the split between innovators and imitators and shows how each group drives overall uptake. His model says that:

“The probability of adoption at time t given that adoption has not yet occurred is equal to: cumulative fraction of adopters at time .”

As always, an image shown in Figure 4 can convey the message more effectively than a lengthy explanation.

(FIGURA 4)

Let’s take a closer look at Figure 4. At the start of the diffusion curve, adoption is driven mostly by innovators. Soon after, imitators dominate the uptake. The illustration makes it clear that innovators are a much smaller cohort than imitators. So, who exactly are these two groups?

Certain members of your network tend to be imitators…

Think back to the last time you came across a novel idea or product. Odds are a friend or co-worker was the one who showed it to you. In classic diffusion studies, that places you in the imitator category. More recent researchers phrase it more gently: most people want firsthand exposure before they feel at ease adopting something new. Psychologists describe the same impulse as a need for social proof.

In Bass’s view, your adoption was spurred by internal social cues—likely a friend’s recommendation or seeing the innovation in action.

Certain individuals fall into the innovator category…

Think of the occasions when you were the very first in your circle to try something new. Perhaps you were in direct contact with the original creator, or maybe you’d been actively hunting for a fix to a problem.

Acting as an innovator usually means an influence from outside the immediate network spurred you on an advertisement, your own research, or links to another community where the idea had already taken root.

With that backdrop, let’s examine the Bass model’s two key parameters p and q commonly called the Bass coefficients.

Bass coefficients

In Bass’s model, the two parameters — p and q — capture the roles of the adopter groups: p reflects the proportion of innovators, while q measures the influence of imitators.

If you revisit Figure 4, you’ll notice the innovator slice is drawn larger than it really is so it stands out. In reality, innovators form a very small fraction. Empirical studies put the average innovation coefficient (p) at about 0.03, whereas the imitation coefficient (q) averages around 0.38. With these typical values, the Bass equation is usually written in the following form:

“ cumulative fraction of adopters at time .”

Some researchers argue that today’s digital environment should push both coefficients upward q because faster, cheaper communication and dense online networks amplify imitation, and p because discovery and trial have become almost frictionless. Yet, despite these expectations, Bass’s original parameters continue to line up with real-world adoption data remarkably well.

If you’d like to dive into the maths behind the Bass model, I explore the equation in greater detail in another article, which also provides an Excel version of the model for you to experiment with.

Simply splitting the market into two segments doesn’t fully capture the diffusion challenges. For richer insight, we can instead apply Rogers’ framework of five adopter categories.

Rogers’ Five Adopter Categories

The seminal work Diffusion of Innovations by Rogers remains the go-to source on diffusion theory, packed with enduring insights. One of its most useful contributions is the breakdown of adopters into five distinct categories: innovators, early adopters, early majority, late majority, and laggards.

The innovators and early adopters effectively align with Bass’s “innovator” group, while the other categories correspond to his “imitators.” With this mapping, we can redraw the network as illustrated in Figure 5.

(FIGURA 5)

Although Rogers frames them as adopter categories, each group shapes how diffusion unfolds. Let’s start by looking at the innovators.

Innovators

Rogers observed that a tiny slice of the population—about 2.5 %—acts as the innovation’s entry point. Dubbed innovators, these individuals are comfortable with risk and often maintain close links to the industry or the original creators. Because they adopt early, they serve as the conduit through which awareness of the new idea spreads to the rest of the network.

Early Adopters

Roughly 13.5 % of your audience belongs to the early-adopter segment. These individuals are nearly as adventurous as innovators but prefer to see a bit of market traction before committing. Closely linked to innovators, they often act as opinion leaders or thought-shapers, forming the critical bridge that carries awareness of the innovation deeper into the network.

The Early and Late Majority

Following the early adopters, we encounter the two largest segments of adopters: the early majority, which represents 34% of your target audience, and the late majority, accounting for an additional 34%.

The Average Customer Belongs to the Early Majority

Members of the early majority are typically connected to early adopters, but they delay adoption until they have seen some evidence of the innovation’s success. This group is where your average customer is most likely to be found.

In contrast, the late majority approaches adoption with a degree of caution. They require even more reassurance, often waiting until the innovation has been widely accepted before they decide to adopt. This group only commits after the average customer has already embraced the innovation.

Laggards

Lastly, we encounter the most resistant group of adopters: the laggards, who make up 16% of your customer base. These individuals are generally reluctant to embrace change and are the most difficult to persuade.

Although laggards are part of your social network, they may not truly align with your target market. Their adoption typically occurs long after the majority has accepted the innovation, often due to necessity rather than choice.

It’s worth noting an important insight from Moore (which we’ll explore further below):

”…any of the adopter groups will have difficulty accepting an innovation if it is presented to them in the same way as the group to the immediate left.”

Moore, Crossing the Chasm

Adapting the Approach for Different Adopter Groups

In other words, we can’t introduce an innovation to the early majority in the same way we present it to early adopters. These two groups have different motivations and decision-making processes, which means a one-size-fits-all strategy won’t work.

This idea is further emphasized by Maloney’s 16% rule, which highlights a critical point of transition between early adopters and the broader majority—something we’ll explore in more detail later.

One common way to visualize these adopter types is through Rogers’ adoption curve, which clearly illustrates the distribution of how different segments embrace innovation over time.

Rogers’ Adoption Curve

We can visualize the adoption rate across a social system over time (see Figure 6). This graphical representation is known as Rogers’ Adoption Curve, and it illustrates the predicted percentages of the target market for each adopter type.

Figure 6

Rogers defined the size of each adopter group using standard deviations drawn from his extensive analysis of existing research. These proportions serve as a practical rule of thumb for understanding how innovations spread within a market.

What’s particularly notable is that Bass’s further research yielded results that align closely with Rogers’ model, providing strong support for its reliability.

Relation of Rogers’ adoption curve to Bass’ Diffusion model

Rogers’ adoption curve provides a clear, broad understanding of adopter categories within a market. However, what’s particularly intriguing is Bass’s 1969 mathematical model of diffusion, which takes this concept a step further. His model is an evolution of his 1962 imitation model, and I dedicate an entire section to it.

For now, it’s worth noting that Bass’s model generates curves strikingly similar to Rogers’ adoption curve. By adjusting the coefficients in Bass’s model, it can be precisely tuned to match actual adoption data.

Take a look at Figure 7, where real sales volumes of various technologies are shown, overlaid with the adoption curve—demonstrating just how accurately the model maps to reality.

Figure 7

One key outcome of Bass’s model is that it offers more accurate values for the sizes of Rogers’ adopter categories, compared to the original approach using standard deviations. For instance, while Rogers suggested that innovators make up around 2.4% of the market, Bass’s model refines this to a range of 0.8% to 2.8%, providing a more precise understanding.

However, before we get too comfortable, it’s important to recognize a critical issue within diffusion studies:

Pro-Innovation Bias

There is a common assumption that all innovations are inherently positive. This pro-innovation bias can distort our understanding of diffusion, leading us to overlook the fact that some innovations may be flawed, unnecessary, or even harmful. Recognizing this bias is essential for a balanced view of how new ideas spread.

”…an innovation should be diffused and adopted by all members of a social system, that it should be diffused more rapidly, and that the innovation should be neither re-invented nor rejected.”

We often assume that once someone within a network learns about an innovation, they will adopt it and help spread it further. This belief mirrors how viral infections, like COVID-19, rapidly spread worldwide between 2019 and 2021.

However, this assumption reveals a pro-innovation bias, where research tends to focus on adoption rather than resistance. But diffusion isn’t always guaranteed—it can fail.

As Moore highlights, the risk of failure is especially high at the boundaries between adopter categories particularly between early adopters and the early majority, a critical gap he famously calls “Moore’s Chasm.” This is where many high-tech innovations stumble, unable to transition from a niche audience to the broader mainstream.

Crossing the Chasm – A Diffusion Challenge

One of the reasons diffusion often fails at the boundaries between adopter categories is that internal influences work best among similar individuals. People are more likely to be persuaded by others who share their values, interests, or needs.

With his background in marketing, Moore emphasizes this point, noting that:

”…any of the adopter groups will have difficulty accepting an innovation if it is presented to them in the same way as the group to the immediate left.”

For high-tech innovations, the transition between early adopters and the early majority is not just a gradual shift—it’s a significant gap. This is the point where external influences (marketing, media, expert opinions) start to lose power, and internal influences (peer recommendations, user experience) become far more critical.

Unlike early adopters—who are often willing to take risks and explore new ideas—the early majority is more pragmatic. They need clear evidence of value and reliability before committing.

Geoffrey Moore recognized this gap as so substantial that he named it “the Chasm”, making it the focus of his book “Crossing the Chasm.” He explained why this chasm exists, the factors that create it, and strategies for successfully crossing it.

Moore also redefined Rogers’ adopter categories with his own terms, such as referring to early adopters as “visionaries.” (See Figure 8).

Figure 8

According to Moore, the early majority (pragmatists) are fundamentally different from the early adopters (visionaries)—and this difference is a key reason for the Chasm. Pragmatists often hesitate to trust or follow visionaries because they see them as:

• Disregarding practical experience: Visionaries often overlook the value of proven methods and industry best practices.
  
  
• Prioritizing technology over real-world application: Visionaries are drawn to cutting-edge solutions, even if they lack immediate relevance.
  
  
• Neglecting existing infrastructure: They may introduce innovations without considering compatibility or integration with established systems.
  
  
• Focusing on disruption: Pragmatists are wary of the instability that high-tech innovations can bring.
  
  

From a network viewpoint, the problem is that information about the innovation doesn’t effectively travel between these two groups. The communication channels that connect visionaries to pragmatists are weak or inefficient (see Figure 9), making it difficult for positive experiences to be shared.

As a result, the diffusion process is at serious risk of stalling before reaching the broader market.

Figure 9
To overcome the challenge of crossing the chasm, Moore proposes several strategic approaches, often illustrated through memorable analogies like “bowling alleys” and “tornadoes.” We will explore these strategies in detail shortly.

But before diving into those tactics, let’s gain some additional insight from another market.

Maloney’s 16% Rule

While Moore explains the chasm as a result of the early majority’s distrust of early adopters, Maloney offers a different perspective: the problem lies with early adopters being unwilling to share what they’ve discovered.

Drawing on the six principles of persuasion from Robert Cialdini’s “Influence: The Psychology of Persuasion,” Maloney identifies a key difference between these groups:

• Innovators and early adopters are driven by scarcity. They seek exclusive, cutting-edge solutions—things others don’t yet have.
  
  
• The early majority is motivated by social proof. They want what they see others using and benefiting from.
  
  

This insight highlights a critical transition point in diffusion: moving from scarcity-driven adoption (early adopters) to social-proof-driven adoption (early majority). It’s a shift from exclusivity to acceptance, and understanding this difference is essential for crossing the chasm effectively.

Figure 10

Recognizing the distinct motivations of different adopter groups—and aligning with Moore’s insight that each group must be persuaded differently—Maloney introduced his 16% rule, which states:

”Once you’ve reached 16% adoption of any innovation, you must change your messaging and media strategy from one based on scarcity to one based on social proof, in order to accelerate through the chasm and the tipping point.”

Maloney’s 16% rule

Maloney’s 16% rule highlights that marketing must change as adoption grows. For the first 16%—innovators and early adopters—emphasize scarcity through PR, insider access, and exclusivity. Once adoption hits 16%, shift to social proof, using mass media and testimonials to show that others are benefiting. If we cross this chasm, the next goal is the tipping point—where adoption accelerates rapidly.

The Tipping Point and Diffusion

The tipping point is the moment when your innovation moves from gradual adoption to rapid, widespread acceptance. It typically occurs within the early majority, where diffusion accelerates, transforming your innovation from a niche idea to a mainstream success.

Figure 11

Both Moore and Gladwell explore the concept of the tipping point, but from different perspectives. In Moore’s “Inside the Tornado,” he describes how high-tech innovations move through three stages: the bowling alley (niche markets), the tornado (explosive growth), and Main Street (stable, widespread adoption).

In contrast, Gladwell’s “The Tipping Point” views it through the lens of epidemics, emphasizing that reaching the tipping point requires engaging three key types of people: connectors (who spread the idea through their vast networks), mavens (who provide trusted knowledge), and salespersons (who use persuasion to drive adoption).

Bowling Alleys, Tornados, and Main Street

Moore’s strategy for crossing the chasm begins with establishing a beachhead market—a specific niche within the early majority. This is the bowling alley stage, where you focus on creating a complete product that perfectly meets the needs of a narrow market segment. Success here means knocking down more niche markets one by one, building credibility and forging strategic alliances.

But once your product gains enough traction, you may be pulled into the tornado—a period of explosive growth driven by a surge of pragmatists adopting your product. At this stage, your strategy must shift dramatically. Instead of customizing for niches, you must focus on scaling up: offering a generic product, leveraging mass marketing, and pushing for commoditization.

Moore’s approach is fundamentally product and market-focused. In contrast, Gladwell looks at diffusion through a network-first lens, emphasizing the role of key individuals—connectors, mavens, and salespersons—in accelerating adoption.

Connectors, Mavens, and Salesmen

In Gladwell’s “The Tipping Point,” we return to the importance of network structures for driving diffusion. Gladwell identifies a “Law of the Few,” which highlights that in any social network, three types of individuals have an outsized impact on spreading ideas:

• Connectors: Highly networked individuals who know a wide range of people across different social circles. They excel at linking people together, even between otherwise disconnected groups. In network terms, they occupy high centrality and bridge structural holes with weak ties.
  
  
• Mavens: Trusted experts who actively seek out and share new information. They are the people others rely on for advice, making them natural sources of word-of-mouth epidemics. In the modern world, they often appear as social media influencers.
  
  
• Salesmen: Persuasive individuals who have a unique ability to convince others. Their skill in communication and charisma makes them powerful advocates for new ideas.
  
  

Gladwell’s framework suggests that for effective diffusion, you should identify the connectors, mavens, and salesmen within your target audience’s network. By engaging these key actors and tailoring your message to each group, you can accelerate the spread of your innovation.

This concept is reinforced by Keller and Berry’s book, “The Influentials,” which argues that “One American in ten tells the other nine where to shop and what to buy.” This further emphasizes the outsized impact of a small group of individuals in shaping opinions and driving adoption.

Is Gladwell Correct?

In 2008, a researcher challenged Gladwell’s view, finding that in most cases, ordinary individuals, not influencers, drove ideas to the tipping point. This suggests that regular people, not just connectors, mavens, or salesmen, can spark widespread adoption.

“A trend’s success depends not on the person who starts it, but on how susceptible the society is overall to the trend – not how persuasive the early adopter is, but whether everyone else is easily persuaded.”

Is the tipping point toast?

Despite this finding, spending on social media influencers continues to rise.

Whether this means influencer marketing is delivering better results or simply that we’re becoming more easily persuaded is up for you to decide.

Wrapping Up

Diffusion is the process of spreading an innovation across a social system over time. This system is essentially a network of your target market, where adoption happens through two main pathways:

• Innovators: A small segment actively seeking solutions, influenced by external sources.
  
  
• Imitators: A larger group adopting based on social proof, driven by internal influences like word of mouth.
  
  

These two groups, along with others, form adopter categories: innovators, early adopters, early majority, late majority, and laggards. Each group has a predictable size, but transitioning between them isn’t seamless—especially in high-tech markets.

The most significant gap is between early adopters (who value exclusivity) and the early majority (who need social proof). To cross this chasm, we must shift our messaging from scarcity-focused to proof-focused and use broader communication channels.