How You Can Speed Up The Diffusion of Innovation With Modern Psychology of Change

Elliot Rogers explained his Diffusion of Innovation Theory through a psychological model of change that is now long out of date. How can a modern understanding of psychology be applied to the diffusion of innovation to speed it up? 

Introduction to the Psychology of Diffusion of Innovation

Why the Psychology of Change Behind the Diffusion of Innovation Matters

In 1962 Elliot Roger’s described the psychology of change behind the diffusion of innovation theory as a linear psychology learning process called the Innovation-Decision Process.

However, modern psychology emphasises how people actually learn through time through learning loops. Active Inference as well as the OODA loops.

If we can better understand and enhance the learning process we can improve the flow of change through a social system. 

What is the Diffusion of Innovation Theory

The Diffusion of Innovation Theory is all about the speed of new ideas and technologies spread through a social system such as a community or organisation. 

Everett Rogers, a sociologist, is credited with popularising this theory in the 1960s. He argued that diffusion is a social process, influenced by how people communicate with each other and learn about new things.

People adopt innovations and change at different rates: Some people are more eager to try new things than others. The theory identifies five adopter categories: innovators, early adopters, early majority, late majority, and laggards.

The Psychology of Learning is a Key Aspect of Diffusion of Innovation Theory: 

Learning is crucial in the diffusion of innovation theory for a few key reasons:

1. Understanding the Innovation: People need to learn about a new idea, product, or behaviour before they can even consider adopting it. Learning helps individuals understand the innovation’s benefits, drawbacks, and how it might fit into their lives.
2. Decision-Making: Learning allows people to make informed decisions about adoption. They can weigh the pros and cons, compare it to existing practices, and assess if it aligns with their needs and values.
3. Spreading the Word: Early adopters who learn about the innovation’s value can become champions, sharing their knowledge with others. This social learning process is vital for diffusion.
4. Overcoming Resistance: New things can be met with scepticism or fear. Learning through clear communication and demonstrations can address concerns and encourage wider acceptance.
5. Adapting the Innovation: As people use the innovation, they learn new ways to adapt it for their specific needs, further promoting its spread and potential for improvement.

In essence, learning bridges the gap between the innovation itself and its adoption within a society. It fuels informed decisions, fosters communication, and allows innovation to evolve and thrive.

The Psychology of the Innovation-Decision Process

Elliot Rogers created the Innovation-Decision process in the 1960s to explain the psychology learning process behind the diffusion of innovation theory. This process is a linear progression through five stages through which an individual or other decision-making group passes:

1. Knowledge: In the first stage the individual is exposed to the innovation’s existence and gains an understanding of how it functions.
2. Persuasion: In the second stage, the individual forms a favorable or unfavorable attitude toward the innovation.
3. Decision: The individual engages in activities that lead to a choice to adopt or reject the innovation.
4. Implementation: The individual puts the innovation to use.
5. Confirmation: In the final stage the individual seeks reinforcement for the innovation decision and according to Rogers may reverse this decision if exposed to conflicting messages.

The Problems With the Innovation Decision Process.

Our understanding of psychology has moved on significantly from the 1960’s. The innovation decision process is now outdated.  It assumes that people are entirely rational when making decisions. It is a linear process and strangely ignores the identity and thinking of the decision maker upon which everything else in Roger’s theory depends.

Ashby’s Law and Adapting Innovation

Ashby’s Law of Requisite Variety tells us how people need to learn how to make the innovation fit into their ways of working and doing things for it to be sustained. Whilst the Innovation-Decision Process treats change as a one off decision rather, than an adaptation process. Meaning changes may often not be sustained.

Instead, an essential process of learning and adaption is required to help us successfully ensure the innovation fits. The innovation needs to successfully adapt to the work we do, the customers we work with, as well as fit it into our own work. So learning to apply an innovation is not a one off process, but a learning loop of developing understanding what works and what doesn’t.

Updating the Diffusion of Innovation Learning Models:

What is Active Inference?

Active Inference is a theoretical framework in cognitive neuroscience and psychology that explains how living organisms, including humans, understand and interact with their environment. It combines principles from Bayesian inference, control theory, and the free energy principle. 

Key Concepts of Active Inference:

1. Perception as Inference:
   • In active inference, perception is viewed as a process of hypothesis testing. The brain continuously generates predictions about sensory inputs based on prior knowledge and experiences.
   • Sensory data are compared to these predictions, and discrepancies (prediction errors) are used to update the brain’s internal models.
2. Prediction and Error Minimisation:
   • The brain strives to minimise prediction errors by updating its beliefs (internal models) or by taking actions to change the sensory inputs to match its predictions.
   • This process is akin to Bayesian updating, where beliefs are adjusted to reduce uncertainty and improve the accuracy of predictions.
3. Action as Inference:
   • Actions are seen as a way to fulfill predictions. The brain not only predicts sensory outcomes but also predicts the necessary actions to achieve desired outcomes.
   • By taking actions that align with its predictions, the brain minimizes sensory surprise and maintains a state of equilibrium.
4. Free Energy Principle:
   • Active inference is grounded in the free energy principle. Free energy principle posits that biological systems strive to minimise free energy. Free energy is described as a measure of surprise or prediction error.
   • By minimising free energy, organisms maintain a stable and coherent understanding of their environment.

What is the OODA Loop?

John Boyd’s OODA loop is a decision making framework originally developed for military strategy, but is actually applicable to all decision making. OODA stands for Observe, Orient, Decide, and Act, representing a continuous cycle that individuals or organisations go through to respond effectively to changing circumstances.

Key Concepts of the OODA Loop:

Observe:

• Sensing and gathering information from the environment. This involves monitoring the situation, noting changes, and collecting information vs expectations.

Orient:

• Interpreting the observed information compared to an internal mental model. Identifying matches and mismatches with the model. 
• This is where you analyse and synthesise the data, make sense of it, and understand its implications.
• The mental model is updated based on the difference between observations, re-orientating the observer with the perceived reality of the world.

Decide:

• Based on the orientation, you decide on a course of action. This is essentially hypothesis testing based on the update mental model. 
• Effective decision-making requires considering the potential outcomes and choosing the strategy that aligns best with your objectives.

Act:

• The final step is to implement the chosen course of action. This means executing your decision and taking concrete steps to influence the environment.
• After acting, you immediately start observing the results, thus re-entering the OODA loop.

Feedback:

• OODA can either be a one off process of change, or a loop.(or even a ‘conceptual spiral’) Whether there is feedback is the difference between it being a loop or not. Making this oft missed step an essential part of the loop learning process.
• If we want learning and adaptation to continue it becomes essential there is feedback to the decision maker.

Key Similarities and Differences Between Active Inference and OODA Loops?

Both these processes have internal mental models, which are tested and updated through Bayesian logic and hypothesis testing at their heart. But there is a crucial difference which is why i mention them both:

• Active Inference: Focused on the psychology of individual decision-making. It describes how individuals use internal models to perceive the world, make predictions, and take actions to minimise surprise. 
• OODA Loop: Focused on fractal decision-making. It emphasises the nested nature of decision-making processes, applicable equally to individuals, groups, and even entire communities and organisations. The loop repeats at different scales, influencing and being influenced by each other.

Both frameworks have many core similarities and offer valuable insights into human behavior, but they address different aspects. The OODA loop provides a strategic framework for making decisions in complex situations, while active inference helps us understand the individual’s internal processes underlying individual perception and action.

Replacing the Innovation Decision Making Process With New Psychologies of Change.

Active inference and John Boyd’s OODA loop are a more accurate and up to date theory of learning and decision-making. Both active inference and the OODA Loop provide a more realistic and flexible representation of how decisions are made in complex, dynamic environments compared to the linear unjustifiably rational and somewhat rigid structure of Rogers’ model.

Emphasis on Existing knowledge.

These models focus on how people learn and make decisions and re-orientate themselves based on their existing knowledge and beliefs about the world.

Contextual and Situational Sensitivity: 

Both frameworks better integrate contextual factors, recognising the importance of situational awareness and environmental influences in decision-making.

Innovations can be evaluated and adopted with a strong consideration of the specific context and changing circumstances, enhancing the relevance and success of the innovation in diverse environments. The bigger the gap between the innovation and people’s experience the longer it would take people to adopt an innovation.

Continuous Feedback and Adaptation: 

These models emphasise continuous feedback and adaptation, allowing for better handling of the uncertainties and rapid changes that often accompany innovation processes. Meaning that people can successfully implement changes in a sustainable way (complying with Ashby’s requisite variety)

Time to Change

Active Inference and the OODA much better explains why people take time to adopt change. Why they might resist change initially or take a while to grasp. They have to update their thinking to re-orientate their thinking by continuously learning about the world. 

More Dynamic Learning Process:

 By incorporating active inference and the OODA Loop, the theory could better capture the dynamic nature of learning about innovations. Individuals wouldn’t just passively receive information but actively seek it out, shaping their understanding through ongoing experience. 

The knowledge that people need about a change alters over time. From a high level summary of the change, to a process they can follow, and finally, to guidance about how to integrate it with their every day experience. The what to do when type of guidance.

Uncertainty Reduction:

 One of the key factors separating the categories of adopter is the amount of uncertainty they have and how much they will tolerate. Active Inference and the OODA loop explain why there needs to be a greater emphasis on reducing uncertainty and prediction errors, which could lead to faster and more adaptive decision-making processes.

Learning Through Testing Experience: 

The implementation phase can be enhanced to be more flexible, allowing individuals to iteratively test and refine the use of the innovation, rather than a one-time, static adoption decision.

10 Recommendations to Improve Diffusion of Innovation From Adopting Dynamic Learning Models:

1. Emphasise Continuous Learning and Adaptation

Recommendation: Design innovation implementation processes that encourage ongoing learning and adaptation rather than a one-time decision.

• Implementation: Create feedback loops where users can provide continuous input about their experiences with the innovation. Use this feedback to make iterative improvements.
• Example: Establish user groups or communities of practice that regularly share insights and experiences, and adjust the innovation based on their feedback.

2. Reduce Uncertainty and Improve Communication

Recommendation: Focus on reducing uncertainty and prediction errors through clear, consistent, and ongoing communication.

• Implementation: Provide detailed information at different stages—initial overview, process guidelines, and integration strategies. Use multiple communication channels to reach diverse user groups.
• Example: Develop comprehensive FAQ sections, tutorial videos, and live Q&A sessions to address common concerns and questions.

3. Leverage Existing Knowledge and Contextual Sensitivity

Recommendation: Integrate the innovation into existing practices and contextual knowledge of users.

• Implementation: Customise the innovation to fit the specific needs and work and experience of different user groups. Acknowledge and incorporate the existing skills and knowledge users have.
• Example: Offer tailored training sessions that show how the innovation can enhance current practices rather than replace them entirely.

4. Foster a Culture of Experimentation

Recommendation: Encourage users to experiment with the innovation and learn from their experiences.

• Implementation: Provide safe environments for users to test the innovation without fear of failure. Support pilot programs and small-scale implementations before full-scale rollout.
• Example: Implement sandbox environments or beta testing phases where users can try out the innovation and provide feedback before it is fully deployed.

5. Incorporate Active Inference and OODA Loop Principles

Recommendation: Use the principles of active inference and the OODA loop to structure the diffusion process.

• Implementation: Design innovation adoption as a cycle of observation, orientation, decision, and action, with continuous feedback and adaptation.
• Example: Regularly update training materials and support resources based on user feedback and changing conditions, ensuring that the innovation remains relevant and effective.

6. Promote Social Learning and Peer Influence

Recommendation: Utilise the influence of early adopters and social learning to drive wider adoption.

• Implementation: Identify and support early adopters who can champion the innovation. Then share their positive experiences with others.
• Example: Create case studies and success stories featuring early adopters and distribute these through internal communications and social media.

7. Align Innovation with User Values and Needs

Recommendation: Ensure that the innovation aligns with the values, needs, and goals of the users.

• Implementation: Conduct thorough needs assessments and involve users in the development and refinement of the innovation.
• Example: Hold focus groups and workshops to gather user input during the development phase and adjust the innovation accordingly.

8. Provide Clear Pathways for Implementation and Integration

Recommendation: Offer clear, step-by-step guidance on how to implement and integrate the innovation into everyday practices.

• Implementation: Develop comprehensive and transparent implementation guides and support tools that outline each step of the adoption process.
• Example: Create interactive implementation checklists and personalized action plans to help users smoothly transition to the new innovation.

9. Address Resistance and Build Trust

Recommendation: Actively address resistance to change and build trust through transparency support and building relationships.

• Implementation: Recognise and openly discuss potential concerns and challenges. Provide strong support and resources to help users overcome these obstacles.
• Example: Host open forums and feedback sessions where users can voice their concerns and receive direct responses from innovation leaders, subject matter experts and change champions.

10. Monitor and Evaluate Progress

Recommendation: Continuously monitor outcomes and the adoption process and evaluate progress to detect early where challenges are in learning and adoption of the new innovation.

• Implementation: Set up waypoints and milestones and outcome measurement and regularly review processes to track the success and challenges of the innovation diffusion.
• Example: Use data analytics to measure adoption rates, user satisfaction, and changes in outcomes, and adjust strategies based on these insights. Be particularly aware of outcomes to understand whether the desired impact is having the desired outcome.

Conclusion

Modern psychological insights can significantly enhance the Diffusion of Innovation Theory, making the adoption of new ideas and technologies faster and more sustainable. By focusing on continuous learning, reducing uncertainty, leveraging existing knowledge, fostering experimentation, and aligning innovations with user needs, we can create more effective change processes. Using principles like Active Inference and the OODA loop, we better understand human behaviour and decision-making, ensuring innovations are quickly and deeply integrated into everyday practices. Embracing these updated models creates the possibility to accelerate innovation adoption and ensure its long-term success.