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From climate action to public health, even widely supported solutions often fail to gain momentum. Researchers at the University of Zurich now show why: people differ in how much social support they need before changing their behavior. Measuring these individual “tipping points” could help make social change campaigns more effective.
Why do widely supported solutions to major problems, such as climate change, so often struggle to gain real traction? A new study suggests that part of the answer lies in understanding why people resist change, and how the combination of their preferences and social networks can help overcome that resistance.
A study published in Nature Human Behaviour by researchers at the University of Zurich (UZH) shows that it is possible to measure people’s individual thresholds for change. This threshold value describes how much social support a person needs before adopting a new behavior.
Personal thresholds vary widely
The research brings together two fields that have traditionally studied social change separately: behavioral science, which examines what drives individual decisions, and complexity science, which looks at how behaviors spread through complex social networks.
“People don’t change in isolation,” says Manuel S. Mariani from the Department of Business Administration. “They respond to what others around them are doing, but the amount of encouragement they need varies from person to person. Some people will try a new idea the moment they hear about it. Others wait until everyone else is doing it.”
Turning choices into measurable tipping points
The researchers adapted tools commonly employed in marketing research. In survey experiments, participants repeatedly chose between options such as energy policies or messaging apps while seeing different levels of social support for each one.
Based on these choices, the team estimated each participant’s personal threshold for change, i.e., how much social backing was needed before they were likely to support a new energy policy or adopt a new app. “This approach lets us infer individual tipping points directly from observed decisions, rather than guessing them,” says business scientist Radu Tanase.
Combining thresholds with network structure
The researchers then tested whether this information could improve real-world interventions. Using extensive simulations on real social networks, they compared different strategies for “seeding” change. One approach, for instance, encouraged a small number of people to adopt a new behavior first, with the aim of creating a critical mass of early adopters and triggering broader uptake.
They found that strategies combining two types of information – social network structure and individual thresholds for change – consistently outperformed approaches based on only one of these factors.
For instance, in scenarios where individuals with high thresholds were less responsive to targeting, the most effective strategy was to target those individuals connected to many others who were already close to adopting the change. In settings where targeting is costly, as in online influencer marketing, the best results came from more sophisticated algorithms that took both network structure and individual thresholds into account.
More effective interventions for social change
These findings are particularly relevant to public policy, climate action and public health. “By identifying who needs just a little nudge and how influence spreads through social networks, interventions can be designed to have a much larger impact,” notes René Algesheimer, Professor of Marketing for Social Impact at the Department of Business Administration.
Although the experiments relied on simplified social signals and real-life situations involve more complex social dynamics, this study marks a promising step toward more effective social change interventions. The authors comment: “Our study suggests that understanding who is ready to change – and who is not – may be just as important as understanding the networks they belong to.”
Contact:
Dr. Manuel Sebastian Mariani
Research Group Leader
URPP Social Networks
University of Zurich
+41 44 634 29 18
manuel.mariani@business.uzh.ch
Literature:
Radu Tanase, René Algesheimer, and Manuel S. Mariani. Integrating behavioral experimental findings into dynamical models to inform social change interventions. Nature Human Behaviour, 16 March 2026. DOI: 10.1038/s41562-026-02417-4.
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Gesellschaft, Medien- und Kommunikationswissenschaften, Pädagogik / Bildung, Philosophie / Ethik, Psychologie
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Forschungs- / Wissenstransfer, Forschungsergebnisse
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