The Big Picture

Adoption is the moment when an individual or organization decides to use your innovation. It’s a core concept in innovation theory, traditionally defined by five adopter types: innovators, early adopters, early majority, late majority, and laggards. Rogers also describes adoption as a five-step process: knowledge, persuasion, decision, implementation, and confirmation.

But this classic view has a flaw—it assumes that innovation is always positive and only needs to be discovered. In reality, adoption isn’t guaranteed. Many innovations face resistance, where potential users may postpone, reject, or even oppose them. Examples like the original Google Glass or Segway show that not every innovation is embraced.

To better understand this, I include innovation resistance and draw from Christensen’s Jobs-to-Be-Done theory, leading to an updated view of the adoption decision process (see the comparison below).

Figure 1

Rogers identified five variables that can significantly speed up the adoption of an innovation within a group:

1. Communication Channels: The methods used to share information about the innovation.

2. Nature of the Social System: The structure and characteristics of the community or network.

3. Perceived Attributes of Innovation: How potential adopters view the benefits and drawbacks of the innovation.

4. Type of Adoption Decision: Whether adoption is voluntary, collective, or imposed.

5. Extent of Change Agent’s Efforts: The level of support and influence provided to encourage adoption.

I propose a sixth variable: Minimising Innovation Resistance. This means proactively addressing potential objections, doubts, or fears that could slow adoption.

Together, these six factors provide a more complete framework for driving faster and more successful adoption.

Figure 2

Platforms like Tinder, Uber, and Airbnb face unique adoption challenges because they must attract and retain two or more distinct user groups (such as drivers and riders, hosts and guests). Adoption by one group depends on the presence and engagement of the other, creating a network effect. This interdependence means adoption strategies must account for the needs, motivations, and resistance of each group separately.

Key Implications

Adoption theory provides valuable insights for innovators:

• Perceived Attributes and Resistance: We must ensure the innovation is seen as valuable and minimize resistance by addressing common concerns.

• Communication Channels: Selecting the right channels is critical, especially when different adopter groups may respond better to different methods.

• Understanding the Social System: Knowing the network structure helps us identify key influencers and effective entry points.

• Adoption Decision Types: Recognizing whether adoption is voluntary, collective, or imposed allows us to adapt our strategies.

• Change Agent Efforts: Supporting users through the change process can accelerate adoption.

When implementing an innovation within an organization, we can shape the change process by managing communication, adjusting the social system, and leveraging perceived value while minimizing resistance.

Ultimately, adoption and diffusion are closely connected—successful adoption within key groups drives broader diffusion.

Innovation adoption is the process by which individuals or organizations decide to start using a new idea, product, or service. As Rogers (2003) noted in Diffusion of Innovation:

“Getting a new idea adopted, even when it has obvious advantages, is difficult.”

In this article, I will explore two main questions:

1. What does adoption mean for individuals and organizations?

2. Can we speed up adoption? If so, how?

We will begin with the classic view of adoption, examining how it works for traditional products. But this exploration will also cover platforms—like Uber, Tinder, and Airbnb—which face unique challenges because they require adoption from two or more user groups simultaneously.

Along the way, we’ll dive into Rogers’ Adoption Curve, the five adopter types, and his Innovation Adoption Decision Process. I will also present an enhanced version of this process that reveals where innovation resistance can emerge and how to overcome it.

Let’s get adopting!

Adopting

Before we dive into the video that brings Rogers’ adoption curve to life, let me first introduce the curve—see Figure 1.

Figure 1

What does Rogers’ curve reveal? It plots, over time, the share of your target market that has embraced the innovation and groups adopters into five distinct segments:

• Innovators (~2.5 %) – adventurous risk-takers who actively seek out novel ideas.

• Early adopters (~13.5 %) – trendsetters and opinion leaders whose endorsement signals credibility.

• Early majority (~34 %) – practical buyers who wait until the solution looks reliable and proven.

• Late majority (~34 %) – cautious followers who adopt only after widespread acceptance and clear evidence of success.

• Laggards (~16 %) – skeptics who prefer the status quo and require significant persuasion—or external pressure—to change.

You might recognise yourself in one category for one type of product and in a different category for another.

Enough theory—let’s see these segments in action in the next video.

Video 1: Adoption in action

Relationship between diffusion and adoption

Diffusion is “the process by which an innovation is communicated through certain channels over time among the members of a social system.”
Adoption—“getting a new idea adopted, even when it has obvious advantages, is difficult” (Rogers 2003, Diffusion of Innovation).

Although we describe them separately, the two are inseparable in practice. If an innovation fails to diffuse, potential users simply never hear about it, so adoption never begins. Conversely, if people stop adopting, diffusion stalls—no one sees the solution in use, no reviews are written, and word-of-mouth dries up.

That’s why understanding diffusion—especially how it unfolds inside a network—matters. In a companion piece I explore Bass’s model, “crossing the chasm,” the 16 % rule, tipping points, influencers, mavens, and more. Network structure (the underlying social system plus its communication channels) often acts as an accelerator for diffusion and, by extension, for adoption.

Problems with adoption theory

Adoption theory has an impressive track record; its S-curves often forecast real-world uptake with almost eerie accuracy. Yet it is not infallible: shifts in media, hyper-connected online communities, and rapid iteration cycles can bend or compress the classic curve—topics we’ll examine next.

Figure

Traditional adoption theory quietly assumes the innovation is inherently valuable—essentially waiting for eager users to embrace it. Reality is messier. Sometimes an innovation offers to solve a problem that few people feel, or one they simply don’t rank as important progress. Segway and Google Glass are well-known examples: technically impressive, yet solving needs that most potential beneficiaries didn’t perceive as pressing.

The theory also under-weights innovation resistance—situations where potential users postpone, reject, or even actively oppose the new idea. Research underscores how common this is:

• “Innovation resistance seems to be a normal, instinctive response of consumers.” — Sheth & Ram (1989)

• Customer resistance is often “one of the most significant risks to innovation.” — Heidenreich & Kraemer (2015)

Because resistance can stall or derail even the best-engineered solution, we need to weave it explicitly into the adoption framework. In the next section we’ll show how resistance surfaces at each stage of Rogers’ five-step Innovation-Adoption Decision Process (Figure 2) and what strategists can do to mitigate it.

Figure 2

1 – Knowledge

The first stage of the adoption-decision process still sits firmly inside diffusion: people must gain knowledge of the innovation. Rogers breaks this knowledge into three layers—awareness, know-how, and know-why:

“Knowledge occurs when an individual is exposed to an innovation’s existence and gains an understanding of how it functions.”
— Rogers (2003), Diffusion of Innovation

• Awareness knowledge arises the moment you first learn the innovation exists—through a trade-fair demo, a magazine snippet, or simply seeing a friend use it.

• If the innovation seems promising, you begin to collect know-how knowledge so you can apply it correctly.

• Should interest deepen, you build know-why knowledge, grasping underlying principles and broader benefits.

Note that resistance can surface even here: if early information is confusing, contradictory, or threatens established routines, many people simply tune out before moving on.

Armed with initial knowledge, you advance to the next stage: persuasion.

2 – Persuasion

In the persuasion stage you gather richer evidence and form a favourable or unfavourable attitude toward the innovation. Reason starts to mingle with emotion as social proof, testimonials, and personal values weigh in.

“Persuasion occurs when an individual forms a favourable or unfavourable attitude towards the innovation.”
— Rogers (2003), Diffusion of Innovation

Here the potential beneficiary judges the perceived value—both functional (time saved, performance gained) and non-functional (status, enjoyment, compatibility with self-image). Innovation resistance is common at this point: doubts about cost, complexity, or social acceptance may postpone or derail adoption unless countered with clear benefits, ease-of-use demonstrations, or endorsements from trusted peers.

If the perceived value outweighs the perceived costs and risks, the decision maker proceeds to the next step in Rogers’ process—decision—otherwise adoption stalls until concerns are resolved.

Figure

2 – Persuasion (continued)

At this stage you also start weighing the innovation’s key attributes. Rogers pin-pointed five variables—relative advantage, compatibility, complexity, trialability, and observability—that strongly influence how fast people adopt. Those factors begin exerting their pull here, just as early feelings of resistance may push the other way.

Still, Rogers reminds us:

“The formation of a favourable or unfavourable attitude toward an innovation does not always lead directly or indirectly to an adoption.” — Diffusion of Innovation (2003)

In other words, a positive attitude is necessary but not sufficient; you must still make the leap to the next stage.

3 – Decision

With knowledge in hand and an attitude—positive or negative—formed, you arrive at the decision step.

Decision is the point at which a potential adopter *chooses either to make “full use of an innovation as the best course of action available” (adoption) or to “not adopt an innovation” (rejection).

Rejection can be:

• Passive – you set the idea aside without strong emotion or public opposition.

• Active (outright resistance) – you decide against it and may even discourage others.

If you opt to proceed, the next phases—implementation and confirmation—determine whether the innovation becomes a lasting part of your routine or is abandoned down the road.9

Figure (gerar outra no chagpt)

But your decision is likely to be more complex.

Addressing Discontinuance

You might choose to adopt an innovation and then later decide to reject it.

Rogers calls this active rejection—also known as discontinuance. We typically discontinue for two reasons:

1. Disenchantment discontinuance – after some time you realise the benefits you expected never materialise.

2. Replacement discontinuance – a newer, better innovation appears, making the original one less attractive.

Insights from Job Theory

We can draw a link here to Christensen’s Job Theory. In that framing we “hire” something to help us make progress. The first, big hire happens when we decide to adopt and use the innovation for the first time. After that, every subsequent use is a little hire. At each little-hire moment we make a fresh decision: do we hire the innovation again (continued adoption), hire something else that helps us make progress better (replacement discontinuance), or choose not to hire the innovation at all (disenchantment discontinuance).

Too often we forget – or worse, never realise – that we must actively address innovation resistance (a topic I explore in detail elsewhere). We assume innovation is inherently positive and focus only on persuading people to adopt. This overlooks the fact that potential adopters can, and frequently do, resist new ideas. Worryingly:

“Adoption begins only after the initial resistance offered by the consumers is overcome.”
— Ram, A Model of Innovation Resistance

Luckily, Ram’s work reminds us that conquering this initial resistance is a prerequisite to adoption. With that in mind, we can refine the decision step to include a resistance-overcoming phase, as illustrated in Figure 4.

FIGURE 4

If we do decide to adopt—and have already overcome the initial resistance—then it is time to move into the next phase: implementation.

4 – Implementation

You begin actually using the innovation in this step, although adopters may still lean on change agents, trainers, or support teams to ease any lingering uncertainty.

It is also here that reinvention tends to appear—that is, users tweak or reshape the innovation as they weave it into their context:

reinvention – “the degree to which an innovation is changed or modified by a user in the process of its adoption and implementation”
— Rogers (2013), Diffusion of Innovation

Reinvention is almost the norm in software. A generic tool might ship with a standard workflow, yet each client tailors modules, fields, or integrations so the solution aligns with their own processes (rather than up-ending those processes to match the tool).

5 – Confirmation

In the final step the adopter seeks confirmation that the original decision was sound:
Does the innovation keep delivering the promised progress?
Are peers still endorsing it?

Positive evidence cements ongoing use; negative cues can trigger disenchantment or a search for replacements.

Increasing the Rate of Adoption

Can we accelerate this entire journey? Historical data suggest it’s already speeding up. Figure 5 (from the cited HBR article) shows that the telephone needed roughly 60 years to reach 80 % penetration in U.S. households, whereas the mobile phone accomplished the same feat in just 14 years. Understanding factors like relative advantage, compatibility, and observability—and deliberately lowering resistance—can shrink adoption timelines even further. — Figure 5

Rate of adoption

“rate of adoption – the relative speed with which an innovation is adopted by members of a social system”
— Rogers (2003), Diffusion of Innovation

Rogers identifies five variables (see Figure 6) that shape how fast an innovation spreads:

1. Perceived attributes of the innovation – relative advantage, compatibility, complexity, trialability, observability

2. Type of innovation-decision – optional, collective, authority

3. Communication channels

4. Nature of the social system

5. Extent of change-agent promotion efforts

Figure 6

figure 8

Communication channels
These are the means by which information about an innovation is transmitted from one person to another. Mass media (TV, newspapers, online articles) can generate broad awareness, but interpersonal channels (peer networks, word-of-mouth, social media communities) are far more influential during persuasion and decision stages, because they carry greater trust and allow prospective adopters to ask questions and see real-world use.

Nature of the social system
This refers to the structure, norms, and degree of interconnectedness within the group considering the innovation. Tightly knit systems with strong norms and frequent interaction (e.g., professional associations, close-knit communities) tend to diffuse innovations more rapidly—once a few key members adopt, the rest follow. Looser, fragmented systems may see slower, more uneven uptake.

Extent of change-agent promotion efforts
Change agents (consultants, sales reps, technical champions) actively work to lower barriers and accelerate adoption by providing education, demonstrations, incentives, and ongoing support. The more intensive and sustained these promotion efforts are—tailored workshops, pilot projects, quick-start guides—the faster and smoother the diffusion and adoption process tends to be.

Figure 9

Relative Advantage
How is this innovation better than the existing solution?

the degree to which an innovation is perceived as being better than the idea it supersedes

Rogers (2003) – Diffusion of Innovation
My modern, service-dominant, definition of innovation talks about how it needs to help the “beneficiary make progress, better than they can currently”. And Vogt (2013) talks about what “better” might be. There it is defined as adding in at least one of several sub-dimensions: economic profitability, low initial cost, savings in time and effort, the immediacy of reward, status, etc.

But as well as having a relative advantage, the innovation benefits from being compatible with what the beneficiary currently uses.

Compatibility
We all come with baggage. Experiences, knowledge, familiarity with our ways of working. An innovation that is compatible with what we do now will be adopted quicker than one that does not.

the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters

Rogers (2003) – Diffusion of Innovation
The classic example is why we use the QWERTY keyboard on computers. On typewriters, such a keyboard layout acts to slow down typists to minimise the letter hammers jamming. We don’t have this problem on computers, so no need to use a QWERTY layout. However, it was a shrewd move—it is familiar, and on introducing computers to the market, we already had a pool of people who could use them.

I think we can broaden this concept nowadays to leverage what beneficiaries are used to in other markets/industries. So not just a computer keyboard replacing a typewriter, but—for example—the use of QR codes from the travel industry in finance applications.

Complexity
The perceived attribute of complexity is fairly obvious. The more complicated something is, the steeper the learning curve—and therefore the harder it is to get people to adopt. So it is advantageous to minimise complexity.

the degree to which an innovation is perceived as relatively difficult to understand and use

Rogers (2003) – Diffusion of Innovation
Complexity feeds into resistance as it increases the perceived risks of the innovation to the beneficiary. One approach to address complexity, if you can’t remove it entirely, is to offer trialability.

Trialability
Allowing adopters to trial an innovation is a way to increase the speed of adoption. It lowers uncertainty and allows for learning by doing.

the degree to which an innovation may be experimented with on a limited basis

Rogers (2003) – Diffusion of Innovation
We are all familiar with this in marketing: free samples of goods, freemium software (use a limited version for free, pay for full functionality), or monthly subscriptions instead of large upfront fees.

A downside can be that adopters uncover flaws that outweigh advantages. However, if the innovator reacts swiftly to address those findings, everyone benefits.

Observability
The easier it is for others to see the innovation in action—or that others are using it—the quicker it is likely to be adopted.

the degree to which the results of an innovation are visible to others

Rogers (2003) – Diffusion of Innovation
Apple’s iPod is an oft-quoted example. Since it spent most of its time in people’s pockets, it was hard for others to see it in use. Apple’s solution was white headphones—very uncommon at the time. Even today, white earbuds signal an Apple product in action.

You can find the details in my article “Innovation Resistance.”

figure 10

In short, we need to minimise physical, economic, functional and social risks.

Am I going to be harmed using the innovation? Is there a chance I can be financially out of pocket—remember the Betamax vs VHS wars? Electric cars are fantastic, but if I mainly drive long distances today, then I face a functional risk. And recall the social risks tied to the first edition of Google Glass.

Secondly, we live comfortably within a set of traditions and norms. Most innovations will challenge these. The greater the departure from existing routines or usage patterns, the higher the likelihood of push-back.

Finally, we all hold perceived images of how things are—or should be. Could Japanese manufacturers have sold top-quality motorcycles when they first entered the US? Initially, the prevailing image said “no.”

The extent of Change Agent’s promotion Efforts

The efforts of change agents vary by the type of innovation-decision—and so do the roles they play.

Change agents’ efforts in an optional innovation-decision

Here, the change agent is initially the innovator’s own messaging, tailored to each adopter type. Over time, adoption momentum shifts to peer influence—Bass’s “innovators vs. imitators” dynamic. Targeting key influencers in the network can accelerate uptake.

Change agents’ efforts in a collective innovation-decision

Collective decisions demand a more formal change-management approach. The group must:

1. Stimulate awareness of the need for change

2. Initiate a structured search for solutions

3. Legitimize the emerging consensus

4. Decide on the innovation to adopt

5. Act to implement it
   (Rogers & Shoemaker, 1971)

Change agents’ efforts in an authority innovation-decision

When adoption is mandated—by regulation or senior leadership—the process shifts fully into formal change management. I recommend Kotter’s 8 Accelerators of Change (formerly “8 Steps of Change”) to guide rollout, ensure buy-in, and sustain the new practice.

Communication channels & Nature of Social System

The rate of adoption is, unsurprisingly, related to how rapidly knowledge of the innovation spreads. And that, in turn, depends on two diffusion-related factors:

• Communication channels – the pathways and topology through which information flows. This includes mass-media versus interpersonal networks, digital platforms, and even the type of messaging you deploy (e.g. leveraging the 16 % rule to trigger a tipping point).

• Nature of the social system – the structure, norms, and degree of interconnectedness within the group. Highly cohesive, norm-driven systems can diffuse an innovation quickly once a few members adopt, whereas loose or fragmented systems often require targeted seeding and repeated reinforcement.

Minimising Innovation Resistance

As we’ve already seen, removing innovation resistance is the path to an adoption decision. Resistance exists on a spectrum—from postponement to rejection to outright objection. My article on resistance dives into the details, but the key takeaway is that we can pinpoint specific barrier attributes that give rise to each form of resistance. To accelerate adoption, these resistance factors should be included alongside Rogers’ original five variables.

Key resistance attributes to include:

• Usage barriers – real or perceived difficulty in learning or integrating the innovation (ties back to complexity).

• Value barriers – when the perceived benefits don’t sufficiently outweigh costs or effort (overlaps with relative advantage).

• Risk barriers – fears of physical, economic, functional or social harm or loss.

• Tradition barriers – the extent to which the innovation conflicts with established routines or norms.

• Image barriers – mismatch between the innovation and the user’s self-image or social identity.

By explicitly measuring and mitigating these resistance attributes—through education, trial programs, endorsements, and design tweaks—we lower the hurdle at the decision step and push the adoption curve upward and to the left.

Multi-sided platforms
Adoption of multi-sided platforms raises some unique challenges. These are systems—like Uber or Airbnb—where two or more distinct groups must adopt for the network to work. If too few riders join, drivers see little value; if too few drivers join, riders won’t come.

You must view your innovation through the lens of each side:

• What value does it deliver to Group A?

• What value does it deliver to Group B?

Each group needs sufficient proposed value (see “What is value?”) before they’ll take the plunge. Unfortunately, there’s still limited research on how adoption and diffusion interplay in truly multi-sided contexts.

Wrapping Up
Innovation adoption remains a foundational theory—with the classic five-step decision process and five perceived attributes shaping the S-curve. By:

1. Embedding innovation resistance into the decision stage,

2. Framing each use as a “job” hire (big and little hires),

3. Applying these insights to multi-sided platforms,

…we gain a more practical, resilient model for driving widespread, sustained uptake.