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  • Writer's pictureFederico Nutarelli

I*nnovating and then? The uncertain world of "innovation diffusion" (an introduction)

Updated: Feb 29

When speaking about innovation diffusion people usually refers to the model of Rogers (1962). Below a brief overview in a short video and a divulgative summary from Sahin (2006):

Rogers' Theory (a summary)

The Core of Rogers' Theory

Rogers' theory breaks down the diffusion process into four key elements: the innovation itself, the communication channels through which information about the innovation spreads, the time it takes for the innovation to be adopted, and the social system that influences this adoption. The theory emphasizes that innovations are not just physical tools but also encompass methodologies and ideas that can reshape educational practices.

The Innovation-Decision Process

The journey of adopting an innovation is segmented into five stages: gaining knowledge about the innovation, getting persuaded of its value, deciding to adopt, implementing the innovation, and confirming the decision based on the results. This process highlights the importance of clear and effective communication, as well as the need for tangible results to solidify the adoption decision.

Attributes Influencing Adoption

Rogers identifies five attributes that significantly affect an innovation's adoption rate: its relative advantage over existing solutions, compatibility with the current system, simplicity in understanding and use (of the innovation), trialability, and the observability of its results. These attributes underscore the need for innovations to be user-friendly and visibly effective to encourage widespread acceptance.

Categorizing Adopters

A fascinating aspect of Rogers' theory is the categorization of adopters into five groups: Innovators, Early Adopters, Early Majority, Late Majority, and Laggards. Each group represents a segment of the population with distinct characteristics and attitudes towards adoption, from the eager Innovators to the cautious Laggards. Understanding these categories helps in tailoring strategies to facilitate the adoption process across different segments of an educational community. Rogers provides also detailed estimates of the percentages of population represented in each category.

The issue

Rogers' theory, while comprehensive and plausible, doesn't lend itself easily to validation through direct observation. In simpler terms, measuring the four attributes that influence adoption and understanding their effect on the spread of innovation is challenging. If you are curious to deepen the discussion feel free to check Iddris (2022) and Lundblad (2003) that I mentioned in the References below.

Additionally, the process for modeling the spread of innovation itself remains somewhat of a mystery. The literature shows very few attempts at tackling this issue, which is unfortunate. Being able to model and predict the diffusion of innovations could benefit not just large companies, but individual users as well. Take the pharmaceutical industry, for example. If we understood how innovations are adopted and spread, we could more effectively reach our target audience and persuade those who are slower to adopt new products.

The limited efforts to model innovation diffusion in the literature typically fall into two categories: network theory, exemplified by works like Zeppini et al. (2013), and microeconomic theory, as seen in the research of Benhabib et al. (2019). Network theory offers a more intuitive framework for understanding how diffusion occurs, while microeconomic theory has become more popular in recent years. This shift in preference is largely because network theory requires detailed data on users' interactions to construct an accurate model, which can be challenging to obtain. On the other hand, microeconomic models focus on broader categories, such as firms, for which data are generally more accessible and easier to gather.

The ideas behind the two streams

Networks Stream

This stream (in a very very very coincise way)  explores how different types of social networks influence the spread of new products, emphasizing the importance of network structure and connectivity. To do so, these stream often uses a concept called "percolation in the price space". This typically constitutes a top-down approach. Let's try to keep it simple...

Imagine pouring water over a sponge and watching it seep through. This process, known as percolation, closely mirrors how a new product or idea filters through society's social fabric. But there's a twist: the journey depends significantly on the product's price and how willing people are to pay for it. Sometimes, the idea spreads like wildfire, reaching far and wide. Other times, it barely trickles through before drying up entirely.

Our society is a tapestry of interconnected networks, from the grid-like patterns of our closest circles to the vast, unpredictable expanse of casual acquaintances and online connections. Researchers categorize these patterns into several types, including:

  • Lattices: Picture a checkerboard where everyone is connected in a predictable, grid-like pattern.

  • Small Worlds: Imagine a neighborhood where everyone knows not just their immediate neighbors but also has a few distant friends in far-off places.

  • Random and Scale-Free Networks: Envision a vast social media network where connections form both by chance and through a few highly popular individuals or "hubs."

Through their studies on this stream, people in academia have discovered fascinating insights:

  • Connectivity is Key: The more interconnected the network, the better the chances of an idea spreading. It's not just about knowing people; it's about how easily we can share something exciting with a wide audience.

  • Clustering Doesn't Always Help: While you might think that tight-knit groups would help spread an idea, it turns out that having a diverse array of loose connections might be more beneficial.

  • Bigger is Better: Networks that allow for multiple points of connection are fantastic arenas for ideas to flourish and grow.

  • Speed Matters: How quickly an idea spreads can depend on the network's "shortcuts" or how easily someone can pass the idea to a distant acquaintance.

One of the most intriguing findings is the role of pricing in the spread of innovation. Starting high might seem counterintuitive, but as the market learns and adapts, adjusting the price can draw in a wider audience over time. This strategy not only caters to early adopters who are willing to pay a premium but also paves the way for broader acceptance as the price becomes more accessible.

Now, even if the model inevitably simplifies the description of Rogers, it helps researchers in validating the latter theory, which is the essence of science. It also reveals some interesting facts! For instance, the role of pricing was not that central (though present) in Rogers' theory!

To summarize...

The study sheds light on the complex dynamics of market diffusion and offers a roadmap for businesses looking to launch new products. By understanding the underlying network structures and adapting pricing strategies over time, innovators can significantly increase their chances of capturing the market's heart. So next time you see a product go from zero to hero, remember: it's not just about the idea itself but how it travels through the intricate web of our social connections.

Some foods for thoughts...

If we are able to model consumers as being nodes of the network, percolation together with other networks measures (centrality, betweenness) may help us uncover which of the consumers belong to which adopters' category! For instance, nodes that, if removed do not allow the spread of the innovation may be considered as hubs and are probably first adopters.

Microeconimcs stream

In an effort to keep our discussion accessible and avoid getting too technical, I've aimed to present these concepts in a more general, informative manner. For those interested in the finer details, I recommend consulting the papers listed in the References section. This discussion primarily explores the link between innovation and economic growth. Scholars in this field typically adopt a bottom-up approach, initially modeling innovation to gauge its effects on measurable outcomes, such as growth. The body of research is gradually expanding to include the dynamic patterns of how innovation spreads. Given that this is an introduction, let's concentrate on the initial aspect of this research.


Imagine a marketplace bustling with firms, each facing a pivotal decision: to innovate and create new technology, to adopt existing technology, or to continue producing with what they already have. This choice isn't trivial; it's about navigating the costs and benefits of adoption, which can widen the gap between the best and the worst technologies in use.

The interplay between adopting new technologies and innovating new ones has a profound impact on the economic field. When firms innovate, they introduce new technologies that stretch the productivity spectrum, pushing the envelope of what's possible. On the other hand, adoption acts as a force of compression, narrowing the gap between the leaders and the followers in technology use.

On this journey, there's a path where growth stabilizes—where the aggregate growth rate mirrors the top speed of innovators. Here, innovation isn't just a driver of long-term growth; it's the engine. Yet, the environment surrounding adoption can significantly influence this engine, either pumping fuel into it or adding friction.

Changes in how technologies are adopted—whether through direct incentives like technology licensing or indirect ones like the potential benefits of future adoption—can alter the innovation landscape. It's a delicate balance: fostering an environment that encourages both innovation and the smart adoption of technology can accelerate growth, while neglecting it can slow the pace.

The Big Picture

This dance between innovation and adoption is more than an academic curiosity; it's a blueprint for understanding how economies grow and evolve. By recognizing the importance of both stretching the boundaries through innovation and compressing the gap through adoption, we can better navigate the pathways to economic prosperity.

In essence, the interaction between innovation and technology adoption isn't just about individual firms making strategic decisions; it's about shaping the trajectory of our collective future. As we look ahead, fostering an environment that encourages the seamless flow of ideas and their adoption is key to unlocking the next wave of growth and opportunity.


Rogers' theory on how innovations spread offers a thorough overview but falls short in terms of practical testing and providing a model to explain the process.

Recent studies are making strides in this area, dividing into two main approaches: a Networks perspective, which looks at the issue from a broader, top-down angle, and a Microeconomic view, focusing on a more detailed, bottom-up analysis.

Although both perspectives contribute significantly towards understanding how innovations spread, there's a pressing need for more work on measuring these processes and validating them with actual data from the real world.


Benhabib, J., Perla, J., & Tonetti, C. (2021). Reconciling models of diffusion and innovation: A theory of the productivity distribution and technology frontier. Econometrica, 89(5), 2261-2301.

Hanafiah, A., Sandi, K., & Aina, A. N. (2023). Does E-commerce adoption create SME performance: A Literature Review. International Journal of Artificial Intelligence Research, 6(1.2)

Iddris, F., Dogbe, C. S. K., & Kparl, E. M. (2022). Innovation education and entrepreneurial intentions among postgraduate students: The role of innovation competence and gender. Cogent Education, 9(1), 2083470.

Lundblad, J. P. (2003). A review and critique of Rogers' diffusion of innovation theory as it applies to organizations. Organization Development Journal, 21(4), 50.

Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432-448). Routledge.

Sahin, I. (2006). Detailed review of Rogers' diffusion of innovations theory and educational technology-related studies based on Rogers' theory. Turkish Online Journal of Educational Technology-TOJET, 5(2), 14-23.

Zeppini, P., Frenken, K., & Izquierdo, L. R. (2013). Innovation diffusion in networks: the microeconomics of percolation.

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