Consumer Judgment and Decision-making in Healthcare Contexts

This dissertation presents two essays that examine individual judgment and decision making in healthcare contexts. Taken together, the findings have important implications for the effective communication of health information and for understanding the role of race in patients' choice of physicians. In Essay 1, we examine the effect of providing morbidity statistics in absolute numbers (vs. absolute numbers and percentages) for diseases varying in prevalence (number of cases) and fatalities (number of deaths). This research stems from observations regarding how media and public health organizations tracked and presented COVID-19 morbidity data for the public. We link the numerical cognition literature to the important domain of consumer perceptions of the health threat posed by a disease and their adoption of preventive behaviors. In multiple studies, we find that when the underlying prevalence and fatality numbers for a disease are provided together with the fatality percentage, cue conflict may arise in some cases. This can lead to important differences in consumer perceptions of the health threat and compliance intent for recommended preventive behaviors relative to those based on the absolute numbers alone. These findings have important implications for the effective presentation of healthcare information. In Essay 2, we examine how racial identity and ensuing race-based thoughts of consumers influence their preferences of same-race primary care physicians (PCPs). We consider two explanations based on race-related psychological processes that drive patient preferences. One explanation is based on a search for cultural compatibility (i.e., shared cultural understanding due to shared race). An alternative explanation is that race-based preferences reflect prejudice (i.e., negative evaluations of medical competency). We distinguish between these two explanations using patients' subjective ratings of cultural competency and medical competency of PCP profiles that are equivalent in all aspects except for race. Furthermore, we use implicit measures (traditional and single-category Implicit Association Tests) to examine the possibility of censored or unstated racial preferences of PCPs. These findings shed light on the role of race in patients' preference for physicians and have important implications for patients, providers, and payers, and inform policy that addresses healthcare disparities.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Business, Marketing

Department dc:contributor.department

Marketing

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Yi, Angela

Chair dc:contributor.committeechair

• Chakravarti, Dipankar

Committee members dc:contributor.committeemember

• Bagchi, Rajesh
• Pandelaere, Mario
• Turner, Broderick L.
• Goenka, Shreyans

• healthcare decision-making
• numerical cognition
• healthcare inequality

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:43127

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/130395