Why does one new idea catch fire across an entire population in months, while another equally useful one sits ignored for years? Think about how quickly UPI payments became normal at the local tea stall, or how a new farming technique spreads from one village to the next. There is a well-studied process behind all of this, and it has a name: information diffusion. It explains how information, ideas, and innovations travel through a society, who adopts them first, and what makes some spread faster than others. Understanding this process is central to library and information science, because libraries and information systems exist precisely to help good information reach people.
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What is information diffusion?
Information diffusion is the process by which a new idea, practice, or technology is communicated over time through certain channels among the members of a social system. The most influential framework for understanding it comes from sociologist Everett Rogers, whose Diffusion of Innovations theory remains the standard reference. Rogers described diffusion as a special type of communication, one where the messages are about a new idea, and where participants create and share information to reach a shared understanding.
Four elements sit at the heart of this definition. First is the innovation itself, which is any idea, practice, or object that people perceive as new. Second are the communication channels through which information about it travels, such as mass media, social networks, or word of mouth. Third is time, because diffusion never happens all at once. Fourth is the social system, the set of interrelated people or groups working toward a common purpose.
A key insight from Rogers is that diffusion is fundamentally about reducing uncertainty. When something is new, people do not have enough information to judge whether it is worth adopting. As a detailed review of Rogers’ theory explains, the decision to adopt is essentially an information-seeking and information-processing activity in which a person tries to reduce doubt about the advantages and disadvantages of the new idea. This is why diffusion matters so much to information professionals. The flow of credible information is the engine that drives adoption.
The innovation-decision process
Individuals do not adopt something the moment they hear about it. They move through stages. Rogers laid these out as knowledge (becoming aware the innovation exists), persuasion (forming an attitude toward it), decision (choosing to adopt or reject), implementation (actually using it), and confirmation (seeking reassurance that the choice was right). At the knowledge stage a person is exposed to an innovation but lacks complete information, and at the persuasion stage they actively seek more, as described in the Canadian Journal of Nursing Informatics. Each stage is an opportunity for information to either move someone forward or stall them.
Patterns of diffusion
When you plot the number of people who have adopted an innovation against time, a clear pattern emerges. Adoption starts slowly, accelerates rapidly, and then levels off as the market becomes saturated. This produces the famous S-curve of diffusion.
The S-curve of adoption
The curve is shaped this way because adoption is socially driven. In the beginning, only a handful of people take the risk, so growth is gradual. Once enough people adopt and start influencing others, the rate of new adoptions surges, creating the steep middle of the S. Eventually, almost everyone who is going to adopt has done so, and the curve flattens. Interestingly, this shape was first plotted long before Rogers. The French sociologist Gabriel Tarde mapped the original S-shaped diffusion curve in the early twentieth century, and later researchers refined the idea, a history traced by the Canadian Journal of Nursing Informatics.
UPI offers a textbook illustration of this pattern. Its growth has been extraordinary, climbing from around 0.8 billion transactions in January 2018 to over 14 billion monthly transactions by mid-2024, eventually handling the bulk of the country’s digital payments. Researchers studying this rise have explicitly used S-shaped growth models to analyse and forecast UPI adoption, treating it as a classic technology diffusion case.
Adopter categories
Rogers observed that people do not all adopt at the same speed, and he divided them into five categories based on how early they embrace a new idea. As summarised by TheoryHub at Newcastle University, these are:
Innovators make up roughly the first 2.5% of adopters. They are venturesome, willing to take risks, and able to cope with uncertainty. Early adopters are the next 13.5%. They are well integrated into their social networks and often serve as opinion leaders whom others watch closely. The early majority, about 34%, are deliberate. They adopt just before the average member of society, once the idea has proven useful. The late majority, another 34%, are sceptical and tend to adopt only under peer pressure. Finally, laggards, the last 16%, are the most traditional and the most isolated, and they adopt only when an innovation has become unavoidable.
Plotting these categories produces a bell-shaped curve that mirrors the cumulative S-curve. The categories are not judgements about people’s worth. They simply describe different relationships with risk and information.
The role of social networks
The steep part of the S-curve depends heavily on social networks. Early adopters act as opinion leaders, and their visible use of an innovation gives the more cautious majority the social proof they need. The critical mass achieved through these opinion leaders is what sparks the “take-off” point in adoption, a dynamic that works within an organisation or across an entire society. This connects to the older two-step flow of communication idea, where information often moves from mass media to opinion leaders first, and then from those leaders to the wider public through interpersonal channels.
Digital networks have amplified this effect dramatically. With UPI, the aggressive merchant campaigns of leading apps created a powerful network effect, where each new user made the system more valuable to everyone else, helping build the critical customer mass that accelerated nationwide adoption.
Factors affecting diffusion
Why do some innovations rocket up the S-curve while others crawl? Rogers identified five perceived attributes of an innovation that, together, explain a large share of the variation in how quickly something is adopted. Crucially, these are about perception, not objective fact. What matters is how potential adopters see the innovation.
Relative advantage
Relative advantage is the degree to which an innovation is perceived as better than what it replaces. This can be measured in terms of economics, convenience, satisfaction, or social prestige. The greater the perceived advantage, the faster the adoption. As outlined in research published on ScienceDirect, this is consistently one of the strongest predictors of adoption. UPI’s advantage was obvious: instant, free, round-the-clock transfers compared to carrying cash or using slower banking methods.
Compatibility
Compatibility is the degree to which an innovation fits with the existing values, past experiences, and needs of potential adopters. An idea that clashes with people’s habits or beliefs faces an uphill battle. One reason UPI spread so widely was its deep integration with the existing banking system and familiar mobile habits, which made it feel like a natural extension rather than a disruption.
Complexity, trialability, and observability
Complexity is the degree to which an innovation is perceived as difficult to understand and use. Higher complexity slows adoption, which is why simple, intuitive designs spread faster. Trialability is the degree to which people can experiment with the innovation on a limited basis before committing. Being able to test something at low risk reassures cautious adopters. Observability is the degree to which the results of using an innovation are visible to others. When benefits are easy to see, word spreads quickly. A study of UPI adoption in India found that ease of use, trust, and perceived benefits were significant drivers of adoption, echoing exactly these attributes.
Beyond the innovation itself, other forces shape diffusion too, including the type of decision involved, the nature of the communication channels, the structure of the social system, and the efforts of change agents who actively promote the idea. Modern researchers also note that newer factors like algorithmic curation now reshape how information spreads, prompting scholars to combine Rogers’ model with network and media theories for the digital age.
Why this matters for information professionals
For anyone working with information, diffusion theory is a practical toolkit. It explains why a new library database might go unused despite being excellent, perhaps it scores low on observability or trialability. It guides how to introduce new services by first winning over the early adopters who influence everyone else. And it reminds us that information does not spread on its own. It moves through people, channels, and time, shaped by perception at every step.
What do you think? If you were tasked with introducing a brand-new digital service in your community, which of Rogers’ five attributes would you focus on first to speed up adoption? And can you identify an innovation you personally adopted late, what finally pushed you across the line?
References
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1496176
- https://files.eric.ed.gov/fulltext/ED501453.pdf
- https://cjni.net/journal/?p=1444
- https://www.orfonline.org/expert-speak/decoding-india-s-upi-phenomenon-a-digital-revolution-with-global-implications
- https://ideas.repec.org/a/aip/access/v4y2023i1p130-148.html
- https://open.ncl.ac.uk/theories/8/diffusion-of-innovations/
- https://paymentscmi.com/insights/unified-payments-interface-upi-sucess-lessons/
- https://www.sciencedirect.com/science/article/abs/pii/S0923474817304927
- https://www.nature.com/articles/s41599-025-05313-w
- https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0334616
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