Academic Experience in the PhD Program

The PhD program’s distinct academic approach is designed to enable students to excel as researchers, educators, and scholars in the future.

Throughout their five-year experience, students focus on one of seven distinct areas of study. Students work closely with PhD faculty and the other students within their field of study, among other scholars and peers across the university. Small class sizes and an intimate faculty-to-student ratio ensures that all students benefit from the attention and expertise of respected experts in their fields. 

Rigorous

The intensity of the full-time PhD program demands energy and zeal in the pursuit of greater understanding, and a commitment to master the behavioral, economic, and mathematical sciences that are the essential components of academic research in business-related disciplines. The curriculum is designed to equip students with the tools to pursue the most challenging research agendas. Residence is required for the majority of the program’s duration.

Flexible

While disciplined and rigorous in training, the program is also flexible for students to pursue their unique research interests. Students customize their program within their chosen discipline to their own goals and research interests.

Collaborative

The collaborative culture of Stanford GSB thrives in the PhD program. Students learn, study, and work together. It’s a dynamic that leads to deep relationships, shared accomplishments, and an enduring support network.

Recent Journal Articles

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Understanding Safety Under High Workload: Adaptive Behavior and Safety-II Mechanisms in Healthcare

Eitan Naveh, Noa Nissinboim, Ryan Leib, Jessica Cleveland, Partha Das, David A. Frank, Craig Bunnell, Sara Singer
Safety Science November2026 Vol. 203

Defaults and Decisions: Choice Architecture and Consumer Opt-Out

Kwabena Baah Donkor
Management Science July2026

Titles Framed as Questions Reduce Reader Engagement

David Fang, S. Christian Wheeler
Journal of Consumer Psychology July2026

Large Language Models Can Predict the Results of Social Science Experiments

Ashwini Ashokkumar, Luke Hewitt, Isaias Ghezae, Robb Willer
Nature July2026 Vol. 656

FIGGsolver: An Interactive Algorithm that Guides the Genealogy Process in Forensic Investigative Genetic Genealogy

Lawrence M. Wein, Malo Sommers
Forensic Science International: Synergy (in press) July2026