Behavioral Intervention Construal: A Framework for Understanding Inferences from Behavioral Interventions

Organizations frequently use behavioral interventions—including incentives, messaging campaigns, and modifications to choice architecture—to influence people’s behavior. However, recent evidence suggests that such interventions often have inconsistent effects across implementations. To understand this variability, we build on the idea that interventions are not simply levers for changing behavior, they also signal useful information to decision makers. For example, when an option is set as the default, people may infer that it is recommended by the organization—an inference that can increase the likelihood that people stick with that option. However, when people perceive that defaults benefit the organization, those defaults may be rejected. Here, we propose a framework to better understand the effects of behavioral interventions by identifying the inferences they prompt people to draw. In developing this framework, we first characterize the defining features of behavioral interventions that determine how they are psychologically experienced. These features implicate five fundamental needs, which guide decision makers’ information-seeking processes. From these needs, we derive a principled typology of inferences drawn by decision makers in response to behavioral interventions, bringing together scattered evidence for these inferences from across the behavioral sciences. The resulting framework entails predictions about when specific inferences will arise and how these inferences influence the overall effects of behavioral interventions. Utilizing the framework, we provide a tool for enhancing intervention design. Taken together, the current research offers an integrative framework for advancing theory on behavioral interventions while also outlining actionable insights for organizations hoping to promote behavior change at scale.