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Creating a Digital-First Strategy
Nonprofit leaders should think less about the technology and more about the people who will use it and the goals they hope to achieve. Part of a series produced with the support of Salesforce.
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The Power of Data
Data and technology can’t exist in a bubble—nonprofits need them to thrive and grow. Hear from several nonprofit leaders about the myriad ways their organizations benefitted from an effective data strategy and system. This video is part of the “Technology for Change” series produced by Stanford Social Innovation Review with the support of Salesforce.
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Driving Social Change With Data
Data is a powerful tool for creating social change, but it can fail to deliver if it lacks rigor or exists in silos. With the right approach, “you can just let the tools do the work,” says the manager of digital infrastructure for the education nonprofit buildOn. Part of a series produced with the support of Salesforce.
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Becoming More Vigilant
An excerpt from See Sooner, Act Faster: How Vigilant Leaders Thrive in an Era of Digital Turbulence explains how to craft and employ vigilance in order to become a better leader.
A Peaceful Cyberspace
Geneva-based CyberPeace Institute offers a beacon of hope for cybersecurity in an increasingly unsecured and dangerous cyberspace. A What's Next article from the Spring 2020 issue.
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The Science of What Makes People Care
Five principles based in social science that will help organizations connect their work to what people care most about.
Innovative Solutions for the Housing Crisis
Six pathways to making housing more affordable and available from the Ivory Prize for Housing Affordability.
The Challenge of Organizational Learning
A recent study found three common barriers to knowledge sharing across nonprofits and their networks, as well as ways and means to overcome them.
The Dragonfly Effect
Two veterans of consumer psychology, marketing, and entrepreneurship provide a guide to using social media for social change.
The Case for Causal AI
Using artificial intelligence to predict behavior can lead to devastating policy mistakes. Health and development programs must learn to apply causal models that better explain why people behave the way they do to help identify the most effective levers for change.