Part 2: How Complex Change Spreads

An empirical look at how the science of change moves organizations from resistance to momentum.

At a Glance: Most transformations fail not because the strategy is wrong, but because leaders misread how change travels through an organization. This case study shows what happens when social science meets strategy — when change is treated as a system to be engineered rather than a message to be managed. Drawing on network science and behavioral economics, a 15,000-person bank used trusted peers as catalysts to accelerate adoption fourfold. The story reveals why change spreads not through compliance, but through credibility; and how the next era of transformation will be built on the science of connection.

The company had grown fast — too fast. In less than a decade, a once-nimble bank had tripled in size to more than 15,000 employees. The structure that once gave it focus now worked against it. Decision-making was slow. Authority sat at the top. And every new initiative, from digital to operational and cultural, seemed to stall under its own weight.

Executives knew they needed a reset. So they did what most firms do: they brought in a global strategy consultancy, built a transformation office, and rolled out a detailed ten-year roadmap. It was just a matter of execution. Every milestone mapped, every dependency tracked.

But something wasn’t clicking.

Like most plans, it assumed change would spread logically, from manager to employee and employee outwards. It didn’t account for the reason most strategies fail: the small, local relationships that shape how people decide whether to follow or resist. Within months, progress had slowed to a crawl. Employees weren’t defiant. They were uncertain.

That’s when our team tried something different. Over the past decade, the social sciences have made remarkable progress in understanding how ideas and behaviors spread. What used to be described vaguely as “culture” or “resistance” can now be mapped, measured, and influenced through network science and behavioral research. The evidence is rigorous and peer-reviewed; what’s new is how rarely it’s applied at scale in corporate transformations.

We decided to bring that science to life. Instead of managing change as a communication cascade, we treated it as a social system that could be engineered. Using these insights, we built an in-house academy — a hybrid learning lab and social network designed to restore clarity and control. Rather than relying on outside trainers, we enlisted trusted peers as “adjunct professors,” creating a model where people could learn new tools and methods from known colleagues already using these techniques. The Academy wasn’t just a training platform; it became the visible infrastructure for how trust and capability spread.

The results moved faster than anyone anticipated. Demand for these sessions exploded. Adoption that was forecast to take a decade accelerated fourfold. For the first time, the organization felt the shift happening from within, not by mandate, but by momentum.

Change Isn’t Linear, It’s Social

For years, business schools and consultancies have told us that 70% of transformations fail. John Kotter’s work gave us a language for that failure, highlighting the need for urgency, vision, and short-term wins. But not a map for how change actually travels through an organization.

The truth is that change doesn’t spread like a virus. It’s more complex and difficult to take root. Behavioral change requires multiple “exposures” before it activates, what social scientists call reinforcement.

That distinction matters. Simple information, like a new policy or tool, may require only a single exposure. But behavioral change, such as learning an entirely new way of working, deepens on repeated exposures from people we already believe and respect. Sociologist Damon Centola calls this a complex contagion.

In other words, organizational change isn’t resisted because people are stubborn. It’s resisted because it’s socially costly. New ways of working challenge old beliefs, habits, and identities. To overcome that cost, people need more than clarity. They need control and enough trusted exposures to reach their threshold for action.

That’s the shift we engineered. We used Organizational Network Analysis to find the people already carrying informal trust. Then we designed behavioral nudges that gave them authorship, letting them adapt pieces of the rollout so it became their story to tell. Once they began teaching their peers, adoption spread naturally, not because of compliance, but because of credibility.

Kotter helped us understand what goes wrong when change fails. Centola helps us see why. When formal process meets social engineering, transformation finally behaves the way it happens in real life: as a network phenomenon.

When Trust Becomes the Accelerator

Every transformation follows a familiar pattern: a few people adopt early, most wait to see what happens, and some hold back until the change is undeniable. Everett Rogers described this pattern decades ago as the diffusion of innovation. What network science adds is the mechanism underneath it. People move at different speeds because they have different thresholds for activation, the point at which enough trusted reinforcement convinces them to act. Some need only one credible signal. Others need many. Once those thresholds are crossed, the spread becomes self-sustaining.

So instead of following reporting lines, we mapped the organization through its relationships. Using ONA, we identified the quiet influencers: the people everyone turned to when things got uncertain. They weren’t always the most senior, but they were the most credible.

That map became our blueprint. We built the Academy around those trusted peers and gave them the stage. Literally. Each became an “adjunct professor,” responsible for teaching others how to navigate the new product-centric model: cross-functional teams, agile practices, and a different way of thinking about ownership.

From a behavioral economics standpoint, that choice was critical. People resist change most when they feel controlled, and comply most when they feel in control. By giving these trusted peers a hand in shaping the rollout, such as adjusting training sequences or designing the curriculum, we created what psychologists call “earned ownership.” Once they began teaching, they were no longer adopting the change. They were the change.

The result was a self-reinforcing loop. Each cohort of learners became another layer of advocates, spreading both the knowledge and the confidence to use it. Learning sessions filled faster than we could staff them. What began as a structured rollout turned into a social movement inside the company.

Within months, the biggest risk to the transformation wasn’t resistance. It was demand. Teams that hadn’t yet gone through the Academy began asking when they could start. They saw how the early adopters were working differently, collaborating faster, and owning decisions that once waited for approval. Every new team that entered the program became visible proof that the change worked.

To keep pace, we went back to the Board, not for a new mandate but for more capacity. We needed additional instructors, curriculum designers, and coordination teams to manage the surge. The Academy soon evolved into something larger: an incubator for both talent and culture. As demand for new skills grew, we began hiring externally directly into the Academy, where new staff would build and teach the next round of courses before rotating into business units. By the time the transformation reached its midpoint, the system was teaching itself.

The roadmap that outside consultants estimated would take a decade was now on pace to complete in less than three years, a fourfold acceleration. The additional staffing costs were easily offset by the productivity gains and the speed of adoption.

What mattered more than the numbers was the tone. The skepticism that marked the first months had dissolved. Leaders no longer had to sell the change; employees were doing it for them. And it wasn’t because the message had changed. It was because the messengers had.

That’s how complex contagions work. They don’t rely on a single spark but on many small confirmations — multiple exposures from people we trust until the new behavior feels safe enough to try. Once that critical mass is reached, change stops being a program and starts being the norm.

Designing for Reinforcement

If change can fail for social reasons, then it can also succeed for social reasons.

That’s the opportunity. Complex contagion isn’t just a theory about how behaviors spread; it’s a playbook for how leaders can make them stick. Once you understand that people adopt new norms through repeated, trusted reinforcement, you can design for it.

In practice, that means shifting focus from communication plans to connection plans. Every transformation needs a formal architecture of clear milestones and defined ownership, but it also needs a social architecture: a map of who people listen to, where trust flows, and how reinforcement happens. Those are the levers that turn intention into behavior.

In the bank’s case, that social system evolved almost organically. The adjunct professors began forming communities of practice, sharing templates and stories from the field. When someone struggled, it was rarely an executive who stepped in. It was a peer from another division who had faced the same issue a few months earlier. Each exchange strengthened the network. Each network made the culture a little more adaptive.

That’s the moment you know transformation has turned the corner: when learning becomes self-sustaining and the system starts to feed itself. The same principles could apply everywhere: mergers, AI adoption, safety culture, even customer experience. Any initiative that asks people to think differently is subject to the same laws of social reinforcement.

Complex contagion gives leaders a way to treat culture not as an abstract force but as an observable, engineerable system. You can see where ideas travel, where they stall, and which nodes (people, teams, or habits) carry disproportionate influence.

It isn’t about manipulation. It’s about precision. When you design change around trust and repetition instead of authority and compliance, you move culture from resistance to resonance.

The Human Algorithm of Change

Change doesn’t fail because people are resistant. It fails because leaders mistake awareness for adoption.

You can tell people what to do. You can even show them why. But until they see trusted peers living it, they’re not ready to follow. That’s the core truth of complex contagion and the simplest explanation for why most change efforts stall somewhere between strategy and execution.

In the bank’s case, nothing about the strategy itself was revolutionary. What changed was how it spread. The moment trusted peers were invited in, resistance turned into reinforcement. The new system stopped feeling imposed and started feeling inevitable.

That’s the untapped power inside every organization: people don’t resist change; they resist being changed. Once they feel heard, once they have a hand in shaping what comes next, they’ll not only adapt, they’ll accelerate it.

For years, transformation has been treated as a management discipline. But the next frontier is social science: understanding how ideas move, where trust lives, and what makes people feel safe enough to try something new. That’s the new competitive advantage.

About the Author: Jason is a behavioral economist and founder of 3Fold Collective, an organizational design firm helping leaders diagnose and reshape cultural dynamics. Visit 3FoldCollective.com to discover more.

References

Centola, D. (2018). How behavior spreads: The science of complex contagions. Princeton University Press.

Greiner, L. E. (1998, May–June). Evolution and revolution as organizations grow. Harvard Business Review. https://hbr.org/1998/05/evolution-and-revolution-as-organizations-grow

Kotter, J. P. (1995, May–June). Leading change: Why transformation efforts fail. Harvard Business Review. https://hbr.org/1995/05/leading-change-why-transformation-efforts-fail-2

Rogers, E. M. (1962). Diffusion of innovations. Free Press.

Welcome to the end! You’re clearly someone who appreciates the messy beauty of social systems. If you’re still curious about what happens when those networks become a source of healing, not just change, take a look at a more numbers-based view of social networks. It’s a story about trust, connection, and what’s possible when culture learns to take care of itself.