DBA Curriculum

Sequence of Courses

Students are required to take five core courses, three specialization courses, and two independent study courses during their first year in the program. All coursework should be completed in the first year. Exceptions will not be made except in very special and extraordinary circumstances.

Also in special cases, students can substitute the core classes with more advanced classes with the permission of the Department Coordinator/Adviser and the DBA Director. No transfer of credits will be allowed. Only students who maintain a B average in their coursework with at most 2 C+ or lower grades will be permitted to take the comprehensive examination. F grade means the student has failed the course and has to retake it. 

  Year 1 Year 2
Fall Semester

Core Course 1 (3 credits)
Core Course 2 (3 credits)
Core Course 3 (3 credits)
Core Course 4 (3 credits)
Independent Study (3 credits)

Dissertation Research (15 credits)
Spring Semester

Core Course 5 (3 credits)
Specialization Course 1 (3 credits)
Specialization Course 2 (3 credits)
Specialization Course 3 (3 credits)
Independent Study (3 credits)

Dissertation Research (15 credits)

 

Independent Study

Students must complete “two independent study” courses. One during the first semester, and the other during the second semester. Students are required to enroll for independent study under the supervision of a qualified faculty member approved by the DBA Director. The faculty member will assign a grade at the end of the course. The independent studies should be aimed at developing a dissertation proposal.

Comprehensive Examination

The purpose of the comprehensive examination is to determine whether the student has acquired sufficient mastery of their major area of study to warrant admission to candidacy. The examination will be conducted by a committee of at least four members of the faculty in the student’s area of interest. Students are required to take their comprehensive examination in June following the end of their first year of coursework. A student who fails this examination may choose to take it a second time latest by August. Students who fail the second time must leave the program; no third attempt is allowed.

Dissertation Research, Proposal, and Defense

To complete the DBA degree, the candidate must pursue an original investigation under the supervision of a qualified faculty member approved by the DBA Director, and present the results in a dissertation. After successfully passing their comprehensive exam, students are required to register for dissertation research in the Fall and Spring of their second year.

Required Core Classes

(5 courses, 3 credits each)

Introduction to Linear Statistical Models, 3 credits (22:960:777)

TBD

Introduction to Probability and Applications, 3 credits (22:960:775)

This is a first course in probability that does not assume any previous knowledge of probability. It will provide an introduction to advanced mathematical concepts and methods that find extensive use in many fields of modern Data Science and Operations Research. The course will have a theoretical focus, but theory will be motivated and illustrated with several examples from areas such as business and engineering.

Introduction to Econometrics, 3 credits (22:223:764)

This is a graduate course in applied econometrics of cross-section and panel data, and some exposure to time series data analysis. The course will provide students with a working knowledge of asymptotic statistical methods and the application of these statistical concepts to study large-sample properties of estimators (defined as the solution to an optimization problem, under various assumptions regarding the true data generating process). The large sample results will be applied to linear and nonlinear (in parameters) generalized least squares (GLS) and maximum likelihood (ML) estimators. These results are extended to develop a nonlinear instrumental variables estimator, the generalized method of moments (GMM) and various asymptotic testing procedures are derived for this general modeling framework. Instrumental variables, panel data, simultaneous equations, discrete dependent, limited dependent and duration models, dynamic panel models, and their applications are covered.

Microeconomics, 3 credits (22:223:752)

TBD

Research Methods (22:620:705)

This is an introductory doctoral seminar on social science research methods in management. The class will examine basic issues involved in conducting empirical research for publication in scholarly management journals. These issues include the framing of research questions, theory development, the initial choices involved in research design, and basic concerns in empirical testing will be considered in the context of different modes of empirical research (including experimental, survey, qualitative, and archival). The class involves discussions and readings that address the underlying fundamentals of these modes, as well as, studies that illustrate how management scholars have used them in their work.

At the end of this course, students should have a broad understanding of how social science research is conducted in management and some of its subfields. The course requirements are also intended to provide you with opportunities to develop your own research ideas and abilities, which requires that you engage productively with the current literature. While the class does not address data analysis techniques in detail, what students learn in this course should allow you to place techniques learned in other courses in context. The hope is that this seminar type of class will be engaging, thought-provoking, and useful for you. Accordingly,  suggestions and feedback about class requirements, readings, and procedures are welcome at any time.

Specialization Courses

(3 classes, 3 credits each)

Accounting

Accounting Theory: Empirical Analysis of Financial Reporting (22:010:751)

The objective of this course is to introduce doctoral students from diverse fields to different mathematical models in the accounting, finance, and taxation literature. Classic and current models from asset pricing, information, contracting and incentives, performance evaluation, taxation will be discussed. The course will be quantitatively oriented, but will tend to focus quite a bit on the modeling intuition and insights.

Accounting Theory: Advanced Topics in Capital Markets and Information (22:010:752)

This course is a discussion and review of selected topics in accounting research implementation, and empirical testing in major fields of accounting. Topics covered include methodology, measurement, testing and econometric techniques in accounting research related areas such as implications of earnings management, conservatism in accounting, topics in international accounting, governmental accounting, managerial compensation among others.

Current Topics in Auditing (22:010:753)

This is an advanced review course in auditing covering both internal and external auditing. Topics include development of modern auditing theory, disclosure problems, principles of managerial control, and operational auditing.

Accounting Information Systems

Advanced Database Systems (22:198:741)

TBD

Electronic Commerce (22:198:721)

TBD

Information Systems Security (22:198:743)

Recent years have witnessed widespread use of computers and their interconnecting networks. This demands additional computer security measures to protect the information and relevant systems. This course prepares the students to meet the new challenges in the world of increasing threats to computer security by providing them with an understanding of the various threats and countermeasures. Specifically, students will learn the theoretical advancements in information security, state-of-the-art techniques, standards and best practices. In particular, the topics covered in this course include: Study of security policies, models and mechanisms for secrecy, integrity and availability; Operating system models and mechanisms for mandatory and discretionary controls; Data models, concepts and mechanisms for database security; Basic cryptology and its applications; Security in computer networks, emerging applications and smart devices; Identity theft; Control and prevention of viruses and other rogue programs.

Machine Learning (22:198:722)

Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, identify fraudulent credit card transactions, and recognize faces or spoken speech. This course will cover supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, hidden Markov models, assessing and comparing classification algorithms, and combining multiple learners.

Finance

Corporate Finance (22:390:761)

The objective of this course is to introduce doctoral students to research in corporate finance. Topics include theory of the firm, capital structure, financial and product markets, initial public offerings, dividend policy, executive compensation, shareholder activism, corporate social responsibility and other related issues.

Investments (22:390:762)

This course introduces students to basic concepts and fundamental theories in financial economics with a particular emphasis on asset pricing. The course content is based on the neoclassical framework and follows the core concepts and major developments of modern finance: starting with expected utility theory and Arrow-Debreu pricing, followed by the static consumption-portfolio problem, eventually leading to no-arbitrage and general equilibrium models. While theory is the main focus, there will also be discussions of empirical methods and evidence whenever they are relevant.

Empirical Finance (22:390:768)

This is a class in Market Microstructure. Market microstructure is the study of how markets operate and how transaction dynamics can affect security price formation and behavior. The impact of microstructure on all areas of finance has been increasingly apparent. Empirical microstructure has opened the door for improved transaction cost measurement, volatility dynamics and even asymmetric information measures, among others. Thus, this field is an important building block towards understanding today's financial markets. The course focuses on empirical methods and models, with special attention to high frequency data analysis.

International Business

Corporate Innovation and International Business (22:553:704)

This course shows how the multinational firm depends critically on its technological and related skills to achieve its central strategic objectives. Introductory classes consider the determinants and characteristics of corporate technological change, and the linkages between science and technology, and the consequences of their geographical localization for international business. Then we assess the contention that corporate strategy should include a strategy for managing innovation, the purpose of which is deliberately to accumulate and exploit firm-specific knowledge. The course examines the implications of technological change as a learning process, for inter-company technology-based alliances, for international technology transfer, and for capturing the returns to innovation in the multinational firm. The innovative records of large and small firms are compared. The use of corporate patent statistics is appraised as a means of measuring patterns of innovation at the firm level. The course concludes with a discussion of systems of innovation, and of technology policies.

Theory of International Business (22:553:701)

This course provides a critical overview of the major theoretical approaches in the international business literature. These strands of analysis can be grouped under the five headings of the market power, internalization, eclectic paradigm, competitive international industry and macroeconomic approaches. We examine both the differences and the scope for complementarities between these alternative means of thinking about international business. Drawing upon this analytical background, the course then reviews the key areas of recent research focus. These crucial new research issues include the role of location in international business, the strategy and organization of multinational corporations, subsidiary level development, cross-border alliances and international mergers and acquisitions. The course concludes with an assessment of the role of methodological design and prospective new directions in international business research.

Global Political Economy (22:553:707)

This course offers a global perspective on long term change in the world economy, and the interaction between countries, regulatory systems and business firms. Attention is especially focused on the dynamics of international trade and investment, including the relationship between trade and economic growth, trade imbalances and protectionism, and the impact of technological innovation on international competitiveness. The role of economic and political institutions is also a central feature of our discussion, including the international trading and financial systems, national systems of innovation and political economy, and the interaction between multinational companies and both the state and multilateral institutions. The course also looks at the possibility of long waves in the world economy, and examines a variety of alternative perspectives on the origins and processes of globalization.

National Innovation Policies and International Business (22:553:705)

This course examines the role of technology in economic development and national innovation systems as they evolve in the globalizing economy.

Marketing

Consumer Behavior (22:630:776)

The purpose of this course is to provide graduate students with a solid foundation for critical thinking and research in psychology, marketing and related topics. Topics of discussion include consumer knowledge (learning, memory and categorization), attitude theory, persuasion, affect and social influence. The course draws from the literature in marketing, psychology and economics. The course will enable students to conceptualize, operationalize, and develop research ideas. Therefore, the focus is on understanding current theoretical and methodological approaches to various aspects of consumer behavior, as well as advancing this knowledge by developing testable hypotheses and theoretical perspectives that build on the current knowledge base.

Marketing Models (22:630:775)

This course covers the basic theory of GLMs and its applications in marketing decision making. Hazard rate and Bass diffusion models are also part of this course. Retailing and financial service examples are adopted for data analysis demonstration.

Qualitative Research Methods (22:630:760)

TBD

Special Topics in Marketing (22:630:785)

This course acquaints students with the current research areas and specific research topics being conducted by RBS marketing and supply chain faculty. It has a pro-seminar format where different faculty members present their areas of research to students every week. Students need to come up with idea papers based on the different topics being presented in class.

One component of this course is about taking an idea and turning it into a published scientific paper. Although the course will impart research skills in design and methodology, its aim is to provide a framework students will carry with them throughout their career as they guide a project from idea to paper. The course will focus on how to take your research ideas and turn them into properly designed studies that can form the basis for high-quality, high-impact scientific papers. The class will give students a solid understanding of study design (i.e., how to make the right choices when testing an idea) and implementation (i.e., how to carry out those choices).

Another purpose of this course is to provide you with an overview of research in the marketing strategy domain including its theoretical foundations, methods, and future research directions. My goal is for you to develop a deep understanding of the field and identify a number of interesting research questions that can form the basis of your research programs. Marketing Strategy is interdisciplinary by definition, thus you will be exposed to theories from a variety of fields.

Other topics covered include probabilistic and recursive economic models and their applications in price theory, distribution channel management, supply chain management and revenue management from a risk analysis perspective. The managerial focus is on place (distribution), price and promotion of the 4Ps under both operational and marketing environments. The course introduces essential demand and supply theories and analytical/empirical modeling techniques to doctoral students, bring them to the research frontier on the SC-MKTG interfaces and grant them ability to publish on both operations management and marketing journals. All data analysis is implemented with R-studio.

Operations Research and Supply Chain Management

Linear Programming (22:711:751)

A survey of linear programming and its applications. Topics include linear programming models, basic simplex method, duality theory and complementary slackness, sensitivity analysis, degeneracy, matrix notation and revised simplex method, special linear programs such as transportation and network flow theory, applications in statistics, economics and finance models of linear programming, game theory, and introduction to interior point methods.

Nonlinear Optimization (22:711:752)

Fundamentals of nonlinear optimization, with an emphasis on convex problems. Gradient, Newton, and other methods for unconstrained problems. Projection, linearization, penalty, barrier, and augmented Lagrangian methods for constrained problems. Lagrangian functions and duality theory. Assignments include computer programming and mathematical proofs.

Operations Analysis (Special Topics in Op. Mgt.) (22:711:785)

TBD

Stochastic Processes (22:960:780)

Review of probability theory with emphasis on conditional expectations; Markov chains; the Poisson process; continuous-time Markov chains; renewal theory; queuing theory; introduction to stochastic calculus, e.g., Ito’s Lemma.

Supply Chain Finance (Special Topics in SCM) (22:799:785)

TBD

Supply Chain Management Strategies (Special Topics in SCM) (22:799:785)

The goal of this course is three-fold: (1) identify problems and key trade-offs in inventory management, (2) introduce the main stream literature that model, solve and understand these problems, (3) bring students to the frontier of this active research area. The course is targeted at graduate students in the areas of operations management, operations research, industrial engineering and management science. To prepare students to do research and to train students for the job market, this course combines lectures, case studies, literature reading and presentations.

Organizational Management

Theory and Research in Organizational Behavior (22:620:755)

Survey of theory and empirical research about the behavior of individuals and groups in organizations. Typical topics include models or organizations (e.g., theories of bureaucracy and closed, open, and natural systems), effects of technology, environment, power and decision-making, organizational culture, motivation, socialization, job design, satisfaction, performance, leadership, group norms, and decision-making processes.

Theory and Research in Organizational Strategy (22:620:756)

This course is designed for doctoral students who expect to conduct research in the strategy area. It surveys and critically evaluates contemporary research in the strategy field, reanalyzing, reframing, and extending traditional approaches and theories.

Negotiations (22:620:717)

TBD

Questions?

For questions about the program, please contact the DBA Office at 973-353-1002 or dba@business.rutgers.edu.

You can also view our Frequently Asked Questions for more information.