Curriculum & Academic Calendar

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 is expected to be completed in the first year. Exceptions may be made in special cases.

Students can take additional advanced classes with the permission of their Adviser and Area Coordinator. 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.

The suggested timeline for completion of the program in two academic years is as follows:

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    Year 1

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

    Core Course 4 (3 credits)
    Core Course 5 (3 credits)

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

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    Year 2

    Dissertation Research (15 credits)

    Dissertation Research (15 credits)

Students who are unable to keep to this schedule can continue in the program for up to four years. Candidates for the degree in the third and fourth years will have to register and pay the annual DBA program and tuition fees for those years.

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 aim of independent studies is to develop research competence in the chosen field of specialization. The faculty member will assign a grade at the end of the course. The independent studies should be aimed at developing a dissertation proposal.

These courses are required of all DBA students, and will be taught by a select group of professors from different business disciplines. The aim of these courses is to expose the students to current research and research tools not only in their individual specializations but also in related fields of business. The aim of these courses is to provide the student the knowledge and skills required to pursue multidisciplinary research and collaborate across business disciplines. The content of these courses may change from year to year, to expose our students to the current state-of-the-art in business research. The specialization courses will be taught in a seminar format.

Each course is assessed based on exam and final paper.

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. This is an examination where the student is required to defend their DBA dissertation proposal. The examination will be conducted by a committee of at least 3 faculty members (including the dissertation adviser) of the Rutgers Business School. All 3 committee members need to have earned doctoral degrees (PhD or DBA), and have to be approved by the DBA Director in advance. Students can only take their comprehensive examination after successfully completing their coursework. A student who fails this comprehensive examination may choose to take it a second time within 6 months or to withdraw from the program. Students who fail the second time must leave the program; no third attempt is allowed.

After successfully passing their comprehensive exam, students are required to register for dissertation research. To complete the DBA degree, the candidate must pursue an original investigation under the supervision of a qualified dissertation advisor and at least two other committee members, The rules for the committee members are the same as for the dissertation proposal committee.

DBA Dissertation Committee

Each student is required to form a dissertation committee of at least 3 qualified faculty members (the dissertation adviser and 2 additional internal full time RBS faculty members). Dissertation committees must be sent to the DBA director’s office for approval in advance. The Dissertation Committee is the candidate’s advising group. The candidate is strongly advised to consult and submit research results to all its members on a regular basis. The committee is expected to regularly review the candidate’s research progress. The completed dissertation must be approved by all members of the committee.

Comprehensive Examination (Dissertation Proposal Defense)

The candidate must submit a written proposal to their dissertation committee members that clearly enunciates the research that will be undertaken, and articulates the contents of the proposed dissertation. It is emphasized that the proposal is the vehicle for communicating the candidate’s project to the committee members. It should provide sufficient detail to allow the committee members to determine the validity and acceptability of the research, in terms of originality, quality and quantity. The dissertation proposal should then be prepared and defended in public. The committee members need to be present for the proposal defense and will be required to evaluate the proposal. The dissertation adviser must inform the DBA director/DBA Office of the time and day of the proposal defense at least two weeks in advance. Upon receiving this information, the Program Office will circulate an announcement of the proposal to all members of the DBA faculty, other faculty, and students who may have an interest in the topic.

Final DBA Dissertation Defense

For the dissertation defense, the same administrative rules apply as the dissertation proposal.

Required Core Classes

(5 courses, 3 credits each)

Each course is assessed based on exam and final paper.

This course discusses linear models and their applications to empirical data — it covers the general linear model; ordinary least squares estimation; diagnostics, including departures from underlying assumptions, detection of outliners, effects of influential observations, and leverage; analysis of variance, including one-way and two-way layouts; analysis of covariance; polynomial and interaction models; model fitting and validation. All data analysis models and approaches will be demonstrated in classes using data of two cases, and will be implemented in R, which is a powerful, extensible and free programming language, gaining popularity for data scientists and business analysts.

This course 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. Students will learn the fundamentals of probability theory including how the concept of probability can be defined and how to calculate probabilities. Discrete and continuous random variables will be introduced, along with standard probabilistic concepts such as mean, variance and conditional probability. Examples of probability distributions will be discussed, including the Bernoulli, binomial, Poisson, uniform and exponential distributions. The normal distribution will be studied in detail, with particular reference to the Central Limit Theorem.

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.

This course is meant to formalize your understanding of how people make economic decisions. We aim to give you the conceptual basis and the necessary tools for learning about the application common Microeconomic tools to strategic decision making. Although pure Microeconomic theory is important, we will attempt to make the subject matter more interesting by using relevant examples involving the application of economic methods in the managerial decision-making process.

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)

  • DBA Seminar I (22:135:701)
  • DBA Seminar II (22:135:702)
  • DBA Seminar III (22:135:703)

Rutgers Business School Asia Pacific, a wholly owned subsidiary of Rutgers, The State University of New Jersey.

CPE/UEN#: 201019419W.
Registration Period:
22 May 2020 to 21 May 2024

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Rutgers Business School Asia Pacific
146 Robinson Road, #07-01/02,
Singapore 068909

Email: info@rutgers.edu.sg
Tel: +65 6532 0083

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