Risk information avoidance – theoretical groundwork and research framework development with a special focus on genetic health risk information
The project’s goal is, therefore, the creation of a generalizable model of risk information avoidance behaviors and a research framework for studying relevant consumer and health behaviors, and generating new knowledge.
Most of judgment and decision-making scholars agree that relevant and quality information is fundamental to forming accurate judgments and making informed decisions. Empirical studies, however, suggest that in real life decision-makers often have a strong preference for knowing less than more. If risk information can improve our lives, why do many decision-makers prefer not to know?
We decided to address the dearth of descriptive theoretical models of risk information behavior by setting the broad, long-term goal of this project to increasing our understanding of how and why consumer decisions to avoid risk information are made. We plan to develop a generalizable framework for studying and interpreting consumer risk information avoidance across a variety of contexts and domains, including predicative genetic testing. To reach this goal, we intend to meet the following list of specific objectives:
- Test and refine our proposed novel, incentives-compatible, experimental framework designed to study information avoidance behaviors in online and lab settings.
- Experimentally evaluate the predictive power of theorized determinants of risk information behavior by verifying the stated hypotheses and conjectures.
In modeling risk information avoidance we will rely on prospect and decision affect theories as well as on findings in experimental psychology, information science and public health. To the best of our knowledge, we are the first researchers attempting to study genetic risk information avoidance in an incentives-compatible fashion. In the absence of studies to base our work on, in developing our fully- functional experimental framework we will have to take an iterative and strongly quantitative approach.
October, 2013 – June, 2016
judgment and decision making, medical decision making, experimental economics, public health, information behavior, health risk information, health behavior, predictive genetic testing, risk information avoidance, emotions