What’s Wrong With The Diffusion Of Innovation Theory?

Diffusion of Innovation Theory is the most widespread and important theory about how technology and change is spread throughout society. But it is more than 60 years old. Is it outdated? What’s wrong with it and what can we do about it?

Introduction

Elliot Roger’s Diffusion of Innovation Theory dates back to 1962 and is still in widespread use today. It has been used and supported by more than 4000 research studies. However, has it stood the test of time? Can we update it with modern ideas and into a useful model to help better understand and manage social and organisational change.

What is the Diffusion of Innovation Theory?

Rogers’ Diffusion of Innovation Theory is a framework that explains how, why, and at what rate new ideas and technology spread through cultures. Developed by sociologist Everett M. Rogers, it was first introduced in his 1962 book “Diffusion of Innovations.” The theory outlines the process by which an innovation is communicated over time among the participants in a social system.

The 5 Categories of Adopter

The Diffusion of Innovation Theory proposed that each innovation would need to be successfully implemented by 5 different groups of adopter across a fixed population.. Once the innovation has been fully implemented by one group the next group of adopters would start their adoption. Together these adopters would follow a normal distribution.

1. Innovators: (2.5%)
2. Early Adopters: (13.5%) 
3. Early Majority: (34%) 
4. Late Majority: (34%) 
5. Laggards: (16%)

The S Curve and the Speed of Adoption of Innovation

The adoption process would follow an ‘S’ curve over time, starting slow, increasing in speed, peaking then slowing in pace. This ‘S’ curve is caused by a combination of communication and social transmission across the social system. Enabling people to hear about and see the innovation being put into action. People choose over time whether or not to engage with the change, but given more opportunities and being exposed to social transmission will change minds.

Modern Applications of Diffusion of Innovations

In today’s fast-paced world, the Diffusion of Innovation Theory can be observed in the spread of technologies such as smartphones, social media platforms, and renewable energy solutions. For instance, the rapid adoption of AI globally (which is being adopted even faster than smartphones) demonstrates how innovations can quickly move through the different adopter categories.

We can see how it started with a small group of experts and enthusiasts in IT, then beyond to people wanting to play with a new toy and learn what they can do. They produced cool stuff so then through mass media, entered the general populations use.

Except that’s not actually true right now. According to a recent study of 12000 people just 7% of people in the US and 2% of people in France and the UK and 1% of people in Japan use it regularly. (Reuters) The real explosion in AI was giving many people access to it. In truth it is still in the innovator or early adopter stage in most countries. The power of this model shows us that they real test of whether generative AI will spread throughout the population is yet to be seen.

Key Strengths of The Diffusion Of Innovation Theory

The strengths of the theory of diffusion of Innovation, has been widely used and tested in research with thousands of studies. The evidence has pointed out some key strengths of the model that can help us:

• Explains Spread of Ideas: It offers a framework for understanding how new ideas, technologies, or behaviours take hold in a society. Research shows it applies to a wide range of fields, from marketing new products to public health initiatives.
• Categorisation of Adopters: The 5 categories of adopters have often been shown to be an effective way of segmenting groups and describing their behaviour in adopting innovation.
• Identifies Key Factors: The theory highlights factors that influence how readily something is adopted. This includes the characteristics of the innovation itself (think: advantage over existing options), the adopter (like their risk tolerance), and how information spreads (social networks, trusted voices). This can help with targeted campaigns to promote adoption.
• Focus on Communication Channels: The theory emphasizes the importance of communication channels in the diffusion process. Research highlights that interpersonal networks and mass media play crucial roles in influencing adoption.
• Social System Influence: It accounts for the social system’s role in adoption, including social norms, opinion leadership, and change agents. Studies show that social influences significantly impact individual decisions to adopt innovations.
• Predicts Adoption Rates: By understanding the factors at play, the theory can help predict how quickly something will be adopted by a population. This has been applied and tested mathematically through the Bass Diffusion Model.
• Identifies Influencers: The theory sheds light on the role of opinion leaders, those trusted individuals who can sway others’ decisions. Research shows targeting these influencers can be key to accelerating diffusion.

Problems and Concerns About the Theory of Diffusion of Innovations:

Pro-Innovation Bias

Pro-innovation bias is the assumptions that innovations are purely good. The idea that all innovations are beneficial and should be adopted. Innovation is something that is done to other people. That there is one right way to do change. Roger’s himself recognised this as a weakness in the theory. This is the old fashioned idea that innovations should be top down. That they are good for everyone.

Since Roger’s wrote his theory the idea of human societies being constantly varying and adapting complex adaptive systems. One size average solutions definitely don’t fit all. Our societies are diverse and applying one size changes and innovations often exasperates existing inequalities. 

Individual Blame Bias

This is the tendency to attribute failure to adopt an innovation through blaming an individual. For example, saying that someone won’t change as they are a laggard. However, individuals are not fixed in their roles. You can be an innovator in one area you have a passion in like buying the latest electric car, whilst being a complete laggard in another area, being slow to adopt a new social media platform.

There are a whole host of reasons as well as the communication of change there are often social cultural and economic issues many of which the individual has little or no control over. By focusing on individual shortcomings, the theory inadvertently places undue pressure on people to conform to new changes without addressing the underlying factors that may hinder successful adoption.

The scientific evidence is that people are actually neurologically wired to blame people when things go wrong. So this is as much as a problem with the way that people use theory than everything else. The theory is pretty clear that this variation is natural in social systems, so blaming individuals whilst human is not in keeping with the theory.

Assumption of Shared Benefits

Are the people making the innovation are going to benefit from the innovation. Do the benefits of the change actually benefit all the individuals in a population? Let’s face it organisations and societies are not always the best at sharing the benefits with the people who do the work. But we want people to choose to change, then we have to make sure people believe and perceive a benefit.

Alongside Benefits Their Can be New Problems

New benefits often mean new problems. What happens if and when people do come across a problem with the innovation? This can create negative modelling, dampening and preventing social transmission, both within and between adoptee categories. We know from the science of ‘loss aversion’ that people are far more effected by loss than benefits.

Destruction of Old Ways

To create space for new innovations means we have to destroy old ways of thinking and doing. Whilst Rogers points out that there is a cost to change he does not really say how costs change the curve. If the old ways are costly to change, people for particular groups will be less likely to change even if the new way is perceived as being better. There appears to be insufficient consideration of the need to create space for change by stopping doing things.

Cost of Innovation

Simon Wardley has pointed out through his Wardley Maps that a key aspect of innovation is it’s novelty. With new innovations having a much higher cost and risk when they are first introduced. After an innovation has become more mature the costs and risks associated with it are typically much lower. Many people hold off purchasing the latest gadgets with the expectations that the cost will come down in future. This must also have a big impact on the adoption rate in particular circumstances. 

How Can We Counter These Challenges:

To counter and address these challenge to the diffusion of innovation theory we need to:

• Involve stakeholders early and often in the innovation process.
• Prototype and test our ideas first with the people we want to adopt the change.
• Understand the real and perceived benefits of change for each group. 
• Map and understand the existing practices and how difficult they are to change across the population.
• Actively create space for change and learning new ways.
• Reduce the cost in time and energy of doing the new way vs the old way. 
• Monitor and address any emerging concerns or barriers to adoption in diverse groups.
• Foster a culture of continuous learning and adaptation.

Recent Improvements and Developments:

1. Lean Startup Methodology: Emphasises rapid experimentation, validated learning, and iterative product releases to better understand and meet market needs, reducing the risks associated with crossing the chasm.
2. Customer Development Model: Developed by Steve Blank, this model complements Moore’s framework by focusing on understanding customer needs, testing hypotheses, and adapting business models accordingly.
3. Agile Development: Promotes flexibility, continuous improvement, and customer feedback throughout the product development process, helping companies adapt quickly to market demands.
4. Digital Marketing and Social Media: Leveraging digital channels to build brand awareness, engage with customers, and gather real-time feedback has become essential for reaching and convincing the early majority.
5. Data-Driven Decision Making: Utilising big data and analytics to gain insights into customer behavior, preferences, and market trends, enabling more informed strategic decisions.
6. Collaborative Ecosystems: Forming strategic partnerships and alliances to create a more comprehensive product offering and enhance market credibility.
7. Complexity Science: Dave Snowden’s Cynefin Framework helps better understand how problems and environments vary, helping us tailor our response.
8. Polarity Management: Barry Johnson’s Polarity Management Shows how when we optimise for one thing, it often creates a tradeoff polarity that needs to be navigated and managed.
9. Active Inference and The OODA Loop: These tells us how we move from seeing adoption of technology as a linear process we instead see it as an active process of adaption re-orientation and learning.

These improvements help technology companies better navigate the challenges of crossing the chasm by increasing their ability to respond to market needs, adapt in an ever changing world, validate their value propositions, and build stronger relationships with their target customers.

Improving the Diffusion of Innovation Process With Modern Psychology

The psychological model behind the diffusion of innovation is process is a very old fashioned model from the 1960s. The model does not take the process of understanding learning and adaption that people need to make when putting a new innovation into practice. If you would like to learn more about speeding up and improving the diffusion of innovation process with modern psychology please read this article here.

Conclusion

The Diffusion of Innovation Theory remains a valuable tool for understanding how new ideas spread within societies. However, it is essential to consider modern challenges and contexts to fully leverage its potential. As we move forward, we need to adopt a more people centric approach to change. Focusing on how different costs and barriers to change affect diverse groups across the population. Whilst creating a collaborative culture of learning and adaption.

How do you think we can improve the Theory of Diffusion of Innovation? Share your insights in the comments below: