PhD in Management
The PhD program at Boston University School of Management trains scholars to develop and sharpen management theories to enhance their contribution to management education and practice. Students acquire advanced knowledge of literature and theory in their area of specialization—the major—as well as solid grounding in a minor (for example, a social science discipline such as sociology, economics, or political science) that broadens their foundation. They also gain theoretical and practical knowledge of advanced research skills, essential for publishing in leading academic journals.
Why a PhD in Management
Extraordinary changes in the global business environment are challenging management schools worldwide. Management education must impart the skills required to respond to technological changes, the information revolution, global competition, and constant shifts in political and social environments. Many of the management models adopted by business are outdated, and thus we need innovative ideas rooted in solid academic research.
The PhD in Management program at Boston University School of Management ensures that students develop an appreciation of the role of their research domain in managerial and organizational contexts, and can translate their learning from scholarly research into effective teaching. Our faculty, which has earned world-wide recognition for its scholarly and applied research, is the School’s major resource for doctoral education. Their commitment to advancing management knowledge through research published in top journals, and improving the quality of teaching, enables them to effectively mentor doctoral students, who, in close collaboration with faculty, are part of the intellectual capital of the School.
Specializations are offered in the following academic areas:
- Information Systems
- Operations & Technology Management
- Organizational Behavior
Sample Research Projects
PhD candidates will have the opportunity to work with top faculty on research projects such as those featured below:
Improved Healthcare Through Machine Learning
We are focused on leveraging recent advances in Machine Learning to improve both the administration and delivery of healthcare. Examples of ongoing projects include: The inference of physician social networks from administrative data, and its use in understanding the impact of insurance structure and coverage restrictions; The prediction of patient re-admissions from administrative, demographic, and chart-level data as well as potential interventions to prevent such readmission; The analysis of the raw data in new HIV blood-testing machines which will be deployed to the developing world, attempting to make them as accurate as contemporary western machines without the need for grid power, refrigerated chemicals, or highly trained technicians. Through the development and refinement of machine learning methods, there are many significant gains to be made in these and related areas.
Information Economics & Intellectual Property
In a Remix economy, how should we allocate credit and $$ to the people who create a composite good? How can we rethink intellectual property to permit more “permissionless innovation” and allow more great software, music, art, and writing to be produced? This project explores several analytic models of credit attribution and also visualization for reused works. We will also run live experiments on remix projects in an effort to develop broad policy implications.
Platform Economics & Strategy (www.platformeconomics.org)
This research stream examines the rising development of platform firms and business ecosystems such as Android, Airbnb, Kickstarter, and Pinterest. How is innovation different in this context? Should crowds displace experts? Can we measure the health of an ecosystem and determine where to intervene? Should governments adopt platform strategies to grow their economies? How can new firms launch and overcome critical mass problems. Can we change the structure of an entire industry by placing strategic bets? This research will involve both econometric analysis of big data and analytic theory development.
Funding and Support for Students
The majority of doctoral students entering the PhD program receive substantial financial assistance, which covers full tuition for four years and a generous stipend.
Furthermore, the doctoral program has a budget to support doctoral student research projects, available via application to a faculty review committee. Students may also submit funding applications to attend professional conferences, where they may build their networks and learn more in depth about the research of colleagues in other universities. Support also comes in the form of faculty mentoring, where each incoming student is assigned to a faculty member in his or her field.