Joseph Reiff Directory Page
Joseph Reiff
Assistant Professor
PhD, Anderson School of Management, UCLA
Professor Reiff studies consumer and managerial judgment and decision-making. He is particularly interested in how organizations can leverage psychological insights to shape consumers' beliefs and behavior at scale. His research examines how consumers interpret and respond to interventions—including incentives, marketing messages, and changes to choice environments. He also studies how managers choose among interventions and how organizations can design them more effectively.
Much of his work focuses on policy-relevant contexts where interventions can have important societal consequences. These include healthcare, where his research examines strategies to improve patient trust and satisfaction, and financial decision-making, where he studies efforts to help consumers save, invest, and prepare for the future.
Reiff's research has been published in the Journal of Marketing Research, Organizational Behavior and Human Decision Processes, Psychological Review, and Proceedings of the National Academy of Sciences. He is a team scientist at the Behavior Change for Good Initiative at Wharton and a member of the Nudge Unit at UCLA Health. He holds a PhD in behavioral decision making from the UCLA Anderson School of Management. Prior to graduate school, he worked at the Federal Reserve Bank of Chicago.
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Recent Research
Behavioral Intervention Construal: A Framework for Understanding Inferences from Behavioral Interventions
Organization Science, August 2026
Managers and policymakers frequently use behavioral interventions—including incentives and messaging campaigns—to influence people’s behavior. They often choose an intervention by asking whether it will make a desired behavior easier, cheaper, or more attractive. Our research suggests they should also ask a second question: “What could this intervention unintentionally communicate?” For instance, interventions can signal that an organization is self-interested or trying to control people’s choices.
The paper introduces a framework for understanding the inferences people draw from behavioral interventions. By identifying these inferences, the framework helps leaders better predict when interventions will succeed or fail, while also broadening evaluation to outcomes beyond behavior, such as trust and satisfaction. Finally, we provide a publicly available AI tool that managers and policymakers can use before launching an intervention to anticipate the inferences people might draw and revise its design accordingly.
Joseph Reiff, Assistant Professor of Marketing, Robert H. Smith School of Business; Jon Bogard, Assistant Professor of Organizational Behavior, Olin Business School