If you build software products, chances are that you’ve worried about adoption before.
Will anyone use what I’ve built? How can I get more people to use it? And why do people leave after a few days?
Many people have written about this problem, and there are indeed many facets to it and many tactics one could employ to nudge people one way or the other. What metrics are the most important ones to track? How should we conduct user research to make sure we solve an actual problem? And how should we buy our ads so we only attract people who’ll be interested in our offering?
But at the core, we need to understand how and why people adopt new ideas, practices, or tools.
There are many different models that explain this. And all of them are probably right to a degree. That’s okay:
All models are wrong; some models are useful.
—George E. P. Box
That is, all models of reality will leave out parts that it doesn’t deem relevant, so they’re all wrong in some regard. But some models allow us to simplify our thinking and thereby let us think things we weren’t able to think about before. That’s how they can be useful.
Today I want to talk about one specific model that explains how people adopt new ideas.
Even if you haven’t heard of this model, you might have heard of a term that it has coined: the early adopter.
The model I’m talking about is that of the Diffusion of Innovations.
It’s a huge field of science, but luckily for us, Everett M. Rogers — who did the initial research and is basically the original creator of this model — has written a whole book that covers many, many studies and provides a great overview. The book is called Diffusion of Innovations [commission earned]. It’s very readable and I highly recommend it.
I used the Diffusion of Innovations theory in my PhD thesis. I really like it: it explains many things about why people behave the way they do, and also gives us clues as to what we could do to change how people behave.
Nowadays I think about software products, their adoption, and user retention a lot — and that keeps the Diffusion of Innovations relevant as ever to me. I keep coming back to it and I keep telling others about it.
This post gives a rough overview of the core concepts of Rogers’ theory, so that hopefully more people learn about it.
Introduction & Definitions
We’ll start by making sure we mean the same things when we use certain words. First up is a central one: diffusion.
Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system.
That right there contains a few other words we might want to define. Innovation is up next:
An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption.
Here’s an important and interesting detail: Rogers emphasizes the perception of newness. When discussing the diffusion of innovations, we don’t care whether an innovation is truly novel.
It just has to be perceived as such.
Innovations are diffused through communication channels:
A communication channel is the means by which messages get from one individual to another.
Many different kinds of communication channels exist, and each may have different properties with regard to the diffusion of innovations through them.
Yet, first and foremost, Rogers identifies two distinct classes of channels: mass media and interpersonal channels.
For example, software developers use Twitter to be exposed to new ideas, but also to connect with other practitioners. In fact, in one study, the median software developer used twelve different channels in their work.
Mass media broadcast messages — such as news, educational information, or entertainment — from a sender to many receivers. Conversely, interpersonal channels exist between individuals and allow for exchanges between them that can go back and forth.
While mass media are initially important to spread awareness about an innovation, interpersonal networks become more important over time as people turn to their peers for opinions on and evaluations of new ideas.
Time is also an important aspect: diffusion is a process that unfolds over time. Thus, time is relevant when investigating how an individual or other unit of adoption gradually changes their internal state (e.g. knowledge or decision to adopt) and overt behavior (actual adoption or rejection).
Time is also an important measure when categorizing adopters into different categories (see below) or when determining an innovation’s rate of adoption — the number of adopters for an innovation in a given period.
Finally, diffusion always happens within a social system.
A social system is defined as a set of interrelated units that are engaged in joint problem solving to accomplish a common goal. The members or units of a social system may be individuals, informal groups, organizations, and / or subsystems.
For social systems, diffusion research distinguishes between two different structures. The social structure influences diffusion through values, norms, roles, and hierarchies. Furthermore, the communication structure determines how messages may flow through the social system, e.g. by providing communication links between individuals.
The Innovation-Decision Process
The innovation-decision process describes how individuals — or other decision-making units, such as groups or communities — adopt or reject an innovation. The goal of this process is to reduce the uncertainty about an innovation.
It is comprised of five steps (see Fig. 1 below) that do not necessarily need to follow each other consecutively.
- Knowledge: The individual becomes aware of the innovation’s existence and starts to understand how it works.
For example, a software developer might learn about test-driven development (TDD) by reading about it in a blog post.
- Persuasion: The individual develops an attitude towards an innovation.
Through a discussion with a colleague that was triggered by the blog post, the software developer realizes that using TDD could be beneficial in her work.
- Decision: An individual who is aware of an innovation and has formed an attitude towards it will at some point decide whether to adopt the innovation. This often involves a trial phase by the individual herself or by a peer.
After the discussion with the colleague, the developer contemplating TDD for her development tries a tutorial she finds on the Web and then decides to start applying TDD from now on.
- Implementation: The individual starts using the innovation. She continues learning about it and overcomes problems, further reducing the innovation’s uncertainty.
The software developer now uses TDD in her daily work and keeps informing herself to improve her application of TDD, for example through exchanges with colleagues who have also adopted TDD.
- Confirmation: After having implemented an innovation, an adopter will continue to collect information that reinforces her decision. If this leads to conflicting information, the adoption may be reversed.
The software developer will constantly monitor herself and her peers to reinforce or refute whether adopting TDD actually does improve the process of developing software in some way.
The passive or active consumption of awareness knowledge and how-to knowledge, the opinions of peers, and personal trials all help a potential adopter in this process. By gradually improving her understanding of an innovation, she reduces the uncertainty associated with ideas perceived as new.
Similar to what happens in a marketing funnel, each stage in this process has the potential for the individual to reject the innovation, e.g. by forgetting about it after the knowledge stage or by simply not acting upon their positive attitude towards the innovation.
The latter phenomenon is called the knowledge-attitude-practice gap (KAP-gap). It describes the situation in which individuals have gained awareness knowledge and how-to knowledge about an innovation, have formed a favorable attitude towards it, but do not act upon it.
It often occurs for preventive innovations: those which can prevent or mitigate an undesirable future event. Because the effect of adopting the innovation is a “non-event” — something not happening — getting access to the benefits of the innovation does not seem as a pressing issue, even when the general attitude towards the innovation is positive.
There’s always a non-zero cost to changing one’s behavior. If an individual isn’t motivated enough to make the change, they won’t want to bear the cost.
An example for software engineering is writing documentation to prevent problems during maintenance: as it is not clear whether there will be maintenance problems (well — of course there will be problems) or whether the developer will be involved in maintenance at all, she may perceive documentation as unnecessary overhead.
A similar effect is at play for other best practices that don’t yield immediate benefits. Why would a developer invest their time into writing comprehensible commit message? There’s no immediate payoff, and especially novices might not yet think as long-term as more senior developers. (Luckily, we can try nudging them using some extrinsic motivators.)
Based on the findings of several studies, Rogers uses a measure of “innovativeness” to distinguish different categories of adopters. Using the average time of adoption for a population and an individual’s time of adoption, the individual can be associated with one of the following five adopter categories. The boundaries between the categories are based on standard deviations from the average time of adoption (cf. Fig. 2).
Again based on studies, Rogers ascribes different characteristics to each adopter category:
- Innovators: Innovators are venturesome and interested in new ideas. They are less connected to their local peer networks, and keep more cosmopolite relationships with other innovators that might be geographically distanced. To support their affinity for novelty, uncertainty, and risk, they need sufficient financial resources, must be able to understand technical concepts, and need to be able to cope with uncertainty.Innovators play an important role in the diffusion of innovations. Their cosmopolite relationships, especially those to other innovators, allow them to import new ideas into their local peer networks. This also makes them gatekeepers or brokers that have control over the flow of innovations between social systems.
- Early Adopters: Compared to the innovators, early adopters are oriented more towards their local peer networks. They are respected by their peers, who often refer to them for advice and information about an innovation.
Early adopters serve as role models for other members of a social system. Once they have adopted an innovation, they communicate their evaluation of it to their peers, who use this evaluation to reduce their own uncertainty about an innovation. Through this process, early adopters can support an innovation in reaching the critical mass that enables the innovation to become adopted more widely.
- Early Majority: A third of the adopters in a social system are in the early majority. They adopt new ideas just before the average member does.
While they do not lead adoption and do not serve as opinion leaders, their interconnectedness in the social system makes them an important link in the diffusion of innovations.
- Late Majority: Just as the early majority, the late majority constitutes a third of the adopters in a social system. They adopt new ideas after the average member has done so. Their reasons for adoption are often economic necessity or increasing peer pressure.
Because of their lower resources, members of the late majority are skeptical about innovations: they need to be sure that the investment will be worthwhile.
- Laggards: Laggards are oriented towards the past and use it as a reference for their decisions. They interact with peers who are similarly traditional as themselves, isolating them from the rest of their social system.
The laggards’ cautious adoption behavior is often based on their limited resources. Before they adopt an innovation, they need to be sure that it will not fail.
Rogers notes that these are ideal types, and that reality shows a continuous spectrum of adopters over time. However, they are a useful abstraction for thinking about the process of diffusion.
As the adopter categories show, an individual’s personal situation and characteristics can influence their time of adoption. Similarly, the next section shows how attributes of innovations themselves can determine their rate of adoption.
Attributes of Innovations
Rogers identifies five attributes of innovations that have a strong influence on whether and how fast an innovation is adopted. He notes that these need not be actual attributes of an innovation — it is only important how a potential adopter perceives the innovation.
- Relative Advantage: The perceived relative advantage of an innovation is the degree to which it is perceived as improving on a previous innovation. This can manifest itself as higher profitability or an increase in social status, for example. Preventive innovations — those whose effects may not be immediately visible, or may never materialize because their purpose is to prevent an undesirable event — are perceived to have a very low relative advantage. Incentives (e.g. money or free samples) can be used to increase the perceived relative advantage of an innovation. However, adoptions motivated by incentives may be less sustainable, with adopters possibly rejecting the innovation when the incentive ceases to be available. Relative advantage is positively related to an innovation’s rate of adoption.
- Compatibility: The perceived compatibility of an innovation describes how consistent it is with regard to an individual’s values, experiences, and needs. The degree of compatibility determines the change in behavior required to adopt an innovation. Thus, instead of introducing an incompatible innovation into a social system, adoption can be easier when the innovation is broken up into several more compatible innovations that can be adopted in sequence — each requiring only a minor behavior change. Compatibility is positively related to an innovation’s rate of adoption.
- Complexity: The perceived complexity of an innovation describes how difficult it seems to comprehend and use the innovation. A high degree of complexity can be a strong barrier against adoption. Complexity is negatively related to an innovation’s rate of adoption.
- Trialability: The perceived trialability of an innovation is the degree to which it can be tried on a probationary basis (yup, as far as I know Rogers made that word up). A personal trial of an innovation is an effective way to reduce uncertainty about an innovation. As such, trialability is positively related to an innovation’s rate of adoption.
- Observability: The perceived observability of an innovation is the degree to which others can observe the results of an innovation. Observing a peer can be a proxy for a trial of an innovation. Observability is positively related to an innovation’s rate of adoption.
These five attributes have been found to determine about half of the variance of adoption rates.
The adoption rate is also influenced by the social system in which an innovation diffuses. Rogers mentions weak ties, opinion leaders, social learning, and critical mass as important concepts that help understand the diffusion of innovations through social networks.
As has been alluded to in the section on adopter categories, many individuals are influenced by peers when deciding whether or not to adopt an innovation. Peers from distant social networks introduce innovators to new ideas. This gatekeeping process gives the relatively locally oriented early adopters access to these innovations. Acting as opinion leaders, they demonstrate the advantages of an innovation to the early majority. Through peer pressure and out of economic necessity, the late majority and laggards finally also adopt the innovation. The diffusion process of an innovation is driven by interpersonal communication.
Research has shown that with high probability, an individual’s close ties are similar to the individual (“homophily”). These peers, in turn, are peers to one another as well. This gives rise to mostly isolated, close-knit cliques. Consequently, new ideas are unlikely to enter such a social system.
However, some individuals in such groups will have ties to individuals from other communities. Because they belong to other peer groups, such connections are often weaker. Yet, these weak ties provide the means for seeding peer networks with innovations. They act as brokers that bridge communities and allow new ideas to flow from one peer group to another.
Thus, while most ties between individuals have a low potential for the exchange of new ideas, the rare and distant weak ties can act as impactful channels in the diffusion of innovations. Close, strong ties are more important when it comes to interpersonal influence.
Interestingly, other studies have shown that the most successful people oscillate between close collaboration with local groups and brokering between groups.
For illustrative purposes, Rogers’ theory divides individuals into opinion leaders and their followers, acknowledging that in reality, this distinction is not as clear-cut. As long as one stays aware of it, it’s still a helpful simplification.
Opinion leaders have exposure to mass media and are cosmopolite. They participate more in their social systems than their followers and have a higher socioeconomic status. Often, opinion leaders are more innovative than their followers — but this depends on whether the social system favors change.
These characteristics give opinion leaders immense influence when it comes to diffusing innovations in a social system. Because their opinions are highly respected, their followers often find them more credible than external influences such as mass media or change agents (i.e., people or organizations that want to introduce change into a social system on purpose).
For this reason, change agents often seek opinion leaders in a social system to help them diffuse an innovation. Rogers cites several studies that have shown that this approach is more effective than alternatives — like, e.g., simply trying to communicate an innovation to all members of a social system.
The observability of an innovation is an important attribute in this regard, as demonstrations by opinion leaders can be impressive “trials by proxy” for a potential adopter.
Social Learning Theory
Bandura introduced social learning theory to explain how individuals learn from each other’s behavior by observations.
This process is called social modeling: based on observing peers, individuals enact similar — not identical — behavior. Instead of imitating others, they adapt an observed behavior to their own situation. If the original behavior leads to an observable reward for the original performer, others can take this as a cue to start modeling their own behavior after the original.
Social modeling can happen through interpersonal networks as well as through public displays, for example through mass media.
The steps Bandura regards as necessary for social learning to happen include attention (the ability to observe a behavior), retention (remembering a behavior), reproduction (i.e., ability to perform a behavior), and motivation.
Social learning and the diffusion of innovations are distinct theories focusing on different things. Yet, they are related in that they both provide a model of behavior change based on communication with others. Both theories regard information exchange an essential factor in behavior change, and both acknowledge ties between individuals as an important facilitator of such exchanges.
Critical mass for an innovation is the point at which its diffusion becomes self-sustaining and does not need to be supported by change agents or similar forces anymore. It is especially important for interactive innovations: Rogers defines these as innovations through which an exchange between individuals is facilitated, and which allow individuals to switch roles.
Examples are many communications technologies, like the telephone, fax, email, or social media sites. They have in common that with each additional adoption, the value of adopting the innovation increases for all past and future adopters.
Since potential adopters are often aware of the fact that the innovation will be more useful if others adopt it, they monitor the adoption behavior of others. Individuals will be more likely to adopt if they perceive that critical mass has been reached, as this increases the innovation’s value (cf. with Reddit who faked a busy community until it was actually busy). Relatedly, opinion leaders are often part of the critical mass, as they are watched by their followers.
Conversely, if an individual believes that others are discontinuing their adoption of an interactive innovation, they will also be more likely to stop using it: discontinuance for such an innovation is equivalent to a decrease in value. This can create cascades of discontinuance that will eventually lead to the innovation becoming abandoned.
Rogers proposes four strategies to support an innovation in reaching critical mass: targeting highly-respected individuals for initial adoption (e.g. Stack Overflow and GitHub also did this); shaping the perceptions of whether critical mass will be reached soon or has already been reached; introducing the innovation first to especially innovative groups, such as R&D departments; and providing incentives for early adoption until critical mass is reached.
The Organizational Innovation Process
So far, we mostly talked about individuals and how they adopt new ideas. However, individuals are often members of organizations and will adopt innovations in such a context.
That’s why I’ll now give a quick overview of the innovation process for organizations (cf. Fig. 3). According to Rogers, it is comprised of the following steps.
- Agenda-Setting: The organization identifies and prioritizes needs and problems that could be addressed by adopting an innovation.
- Matching: The problem identified in the previous stage is matched with an innovation that could solve it.
- Redefining / Restructuring: The organization customizes the innovation according to its own structure, culture, and needs.
- Clarifying: Use of the innovation is starting to diffuse in the organization. The meaning of the innovation becomes clearer for the organization’s members, and they start forming a common understanding of it.
- Routinizing: The innovation loses its distinct quality: it is now part of the organization.
It’s interesting to go through this process with something that one’s own organization has adopted.
Understanding this process also helps one build innovations for other organizations.
During the process of diffusion, an innovation is communicated through communication channels among the members of a social system. The innovation-decision process describes the stages an individual can go through while contemplating the adoption of an innovation: after having gained knowledge about it, the individual forms an opinion about the innovation and decides whether or not to adopt it. The individual then starts using the innovation and further reduces the remaining uncertainty by practice and learning. When the innovation has been adopted, the individual continues to monitor whether adoption still makes sense for her.
Adopters as well as attributes of innovations can be divided into categories established by diffusion research. Their characteristics can provide an estimate of the probability of adoption in a given situation. Social networks have a large influence on the adoption process.
As we saw in the discussion of the KAP-Gap, motivation can be a factor when an individual considers an innovation for adoption. In my next blog post, I’ll discuss Self-determination Theory — a (useful) model of human motivation.