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

Abstract: To systematically change individual behavior, managers frequently use behavioral interventions – including incentives, regulations, messaging campaigns, and modifications to choice architecture. However, recent evidence suggests that such interventions often have surprising and inconsistent effects across implementations. The current research proposes a unifying framework to better understand the effects of behavioral interventions by identifying the inferences people draw in response. The proposed framework begins from the premise that, when encountering such interventions, decision-makers often seek to understand the motives, beliefs, and character of the intervention designer as well as the choices being offered. Specifically, the framework first characterizes 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 then derive a principled typology of inferences that decision-makers draw from behavioral interventions, and we bring together scattered evidence for these inferences from across the behavioral sciences. The resultant framework implies predictions about when specific inferences arise and how these inferences influence the overall effects of behavioral interventions. This allows us to organize findings from the literature and offer untested hypotheses for future research. Leveraging the framework, we provide a publicly available tool for auditing and enhancing intervention design. Together, the current research offers an integrative framework to advance theory on behavioral interventions, while outlining actionable insights for managers hoping to promote behavior change and social welfare at scale.

Keywords: inferences, choice architecture, incentives, intervention design, policy evaluation