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

Abstract

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 start from the premise that interventions are not simply levers for changing behavior; they also signal 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 they stick with that option. But when defaults appear self-serving, people may reject them. Here, we propose a framework to better understand the effects of behavioral interventions by identifying the inferences people draw when encountering them. 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. 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.