Courses and Curriculum
The UF DBA program encompasses 60 credit hours and takes approximately three years to complete. The program is a professional, non-resident program with limited visits to campus allowing participants maximum flexibility to complete their studies without having to sacrifice time away from family and their careers. Over the course of the three-year program, participants will need to make only one or two trips to campus per term. The program consists of the following components:
- Fall of Year 1:
- Managerial Statistics: 5 days, 7 hours of instruction per day (3 credit hours).
- ML/AI & Data Visualization Methods for Research: 5 days, 7 hours of instruction per day (3 credit hours).
- Terms (6): Participants take 6 credit hours in each term for a total of 36 credit hours. The majority of the course content is offered on-campus during the residency weekends. There is a minor online component with one course.
- Research Process: A three-stage process that takes places over the 3 years of the DBA program.
- Advanced DBA Research: Term 7 – choice of discipline (6 credit hours).
- Dissertation: Term 8 & 9 – DBA Doctoral Dissertation/Defense (12 credit hours).
Instructor: Joost Impink
Research Interests: Firm Valuation; Firm Disclosures; Textual Analysis
Topics are related to the construction of variables (based on data in Compustat, CRSP and IBES), econometric issues (dealing with measurement error, scaling issues, clustering of standard errors, testing for multicollinearity, Heckman tests) and special topics (fixed effects regression, propensity score matching, Jackknife procedure, using textual analysis to create new variables) commonly used in finance and accounting, and other research using large archival data.
Instructor: Jim Hoover
Research interests: Artificial Intelligence, Machine Learning and Business Analytics Applications. Applications of these approaches include: Forecasting; Natural Language Processing; Web Scraping; Sentiment Analysis; Recommender Engines; Machine Learning; Artificial Intelligence; Business Analytics Methodologies; Business Analytics Practicum projects.
This is a doctoral-level course introducing AI/ML applications in business. The course focuses on the development of skills that will permit doctoral candidates to replicate data analysis techniques used in top-tier journal articles and to create visualizations most commonly found in these academic publications. The course will begin with a discussion about the differences between AI/ML and theory-based analysis methodologies. These differences create implications for both business implementation and for academic research. Then, the course examines the application of a number of techniques (e.g., regression analysis, ANOVA, interaction effects, designing and interpreting surveys in Qualtrics, and executing experience sampling methodology (ESM) studies) that are commonly used in empirical research. Finally, the course will provide hands-on experience with Excel and other tools to create tables, charts and other visualizations that you will likely need to include in your dissertation.
The course provides insights into how some of the more commonly-used research methods are implemented. By “doing” you will not only gain a better working knowledge of how to utilize the methods, but also insights into what the methods mean and why they are important.
Instructor: Michael Ryngaert
Research Interests: Corporate Finance And Mergers And Acquisitions
The course will begin with the conditions under which financing and payout policy decisions do not matter – the Classic Miller & Modigliani world. The course will then be organized the course around “market imperfections” that make financial decisions important. How do taxes influence financial decisions? How does asymmetric information and agency costs influence decisions. How do potential market inefficiencies affect financing decisions? How do behavioral biases that deviate from “rational economic man” influence decision making? The course will be part lecture, but students will also be given a paper to analyze, critique, and make suggestions for extensions. While exposure to financial theory is important, there will be more emphasis on testing theories using various empirical tools.
Instructor: Andy Naranjo
Research Interests: Financial Economics, International Finance, Asset Pricing, Corporate Finance, Real Estate Finance, Capital Market Linkages and Flows, Information Flows and Processing
This DBA seminar is designed to provide students with an in-depth knowledge of the academic research and practice of international business and finance. Both in and out of class discussions and assignments will provide students with the knowledge and skills they will need to critically evaluate scholarly research in international business and finance and make original research contributions to the corresponding academic and professional areas. Each student should be able to demonstrate a thorough understanding of the extant international business and finance literature and develop an original research paper outline that extends the knowledge in a specific area of international business and finance.
This course covers the core applied and theoretical underpinnings of international business and finance. Drawing on the research related to multinational firms, international trade, and international finance, this course provides a solid framework for all DBA students. Some of the major topics covered include the nature and role of multinational firms, international trade and FDI theory, the impact of globalization, global institutions, features that distinguish international finance from domestic finance, foreign exchange foundations, markets and determination, foreign exchange exposure and management, and international financial markets and investments.
Instructor: Walter Leite
Research Interests: Assessment and Evaluation, Causal Inference, Data Collection and Analysis, Educational Assessment and Measurement, Emerging Technologies, Experimental and Quasi-Experimental Design and Analysis, Longitudinal Data Analysis, Mathematical Modeling, Mathematics Education, Multilevel Model, Online Mentoring, Qualitative Research, Quantitative Research, Research / Program Evaluation, Research Design, Statistics / Applied Stats, Teacher Evaluation, Value-Added Measures of Teacher Evaluation
The objective of the course is to enable students to plan, implement, report, and review statistical analyses involving latent variables under the structural equation modeling framework.
This course will build from the student’s basic knowledge of regression analysis, validity and reliability. It will introduce the modeling of latent variables with confirmatory factor analysis, and assessment of the validity evidence for the hypothesized factor model. Then, it will introduce the estimation of the structural relationships between latent variables using structural equation modeling, including mediation and moderation.
Instructor: Joyce Bono
Research Interests: Leadership, personality, attitudes and motivation, women, quality of work life.
The goal of the course is threefold: a) Develop understanding of theoretical change models and their implications for management, b) refine research skills, particularly those associated with designing and conducting rigorous empirical research and evaluating the quality of published research, and c) develop foundational knowledge on the topics of leadership and motivation, and apply this knowledge to leading effective change and studying the effectiveness of change efforts in organizations.
Instructor: Praveen Pathak
Knowledge of statistics is important for any researcher who needs to extract information from quantitative or qualitative data. My purpose in this course is to introduce statistical tools required for hypothesis testing and linear models. This course should provide you with a package of statistical concepts and procedures that will help you understand how and why statistical techniques work and how to employ them in your research.
Instructor: Brian Swider
Research Interests: The role of motivation in organizations, the role of dispositions and emotions in organizational behavior, Research methods in organizational studies
How do we make sense of people in organizations? What do we know about the organizational context in which we encounter them? And how do we make use of this contextual and interpretational knowledge when we attempt to understand and predict individuals’ behavior? These are some of the questions we will attempt to answer in this course. This course is about the scientific study of human cognition as it is related to organizational studies. The main topic of this course is the knowledge encountered in the workplace and how is it used in activities such as forming impressions of others, explaining individuals’ behavior, and forming managerial decisions. Thus, in this course we will consider topics ranging from basic perception through cognitive organization and interpretation to complex decision making. Theories and methods developed in management, behavioral economics, and cognitive and social psychology will be examined and we will discuss how they can be specifically applied to topics in management. In the process of discussing these topics we will also learn how to use research tools to investigate these issues.
Instructor: Phil Podsakoff
Research Interests: Leadership Behavior and Effectiveness, Substitutes for Leadership, Antecedents and Consequences of Organizational Citizenship Behavior, Relationships between Employee Attitudes and Behaviors, Social Power and Influence Processes, Organizational Research Methods
In their classic book on experimental and quasi-experimental designs for research, Campbell and Stanley (1963) noted that the model for their work was provided by McCall (1923), who stated that although:
“[While]…there are excellent books and courses of instruction dealing with the statistical manipulation of experimental data,… there is little help to be found on the methods of securing adequate and proper data to which to apply statistical procedures… (p. 1)”
Similar points regarding the importance of research methodology (versus statistical analysis) have also been made by Keppel and Zedeck (1991, p. 12), who noted that:
“Though there are numerous techniques of data analysis, no techniques, regardless of its elegance, sophistication, and power can save the research when the design is poor, improper, confounded, or misguided. As we have stated, and will state again, sound inferences and generalizations from a piece of research are a function of design and not statistical analysis.”
Within the context of the above statements, the primary purpose of this seminar is to prepare doctoral candidates to design and conduct research in the organizational and behavioral sciences. Consistent with the focus of the books by McCall (1923) , Campbell and Stanley (1963), and Keppel and Zedeck (1991), the emphasis of this course will be on exploring: (a) the logic of research design, (b) different types of research methodologies, and (c) issues that researchers encounter when using these methodologies. Although the discussion of various analytical procedures will be unavoidable, the major focus of the seminar will be on methodological issues, as opposed to analytical/statistical issues.
Instructor: Aner Sela
Research Interests: Consumer Decision Making, Consumer Behavior
Instructor: Joel Davis
Research Interests: Normative Models of Competition, Services Marketing, Entertainment Marketing, Revenue Management, Markets For Evaluative Information, Models Of Selling And Product Policy, Channels Of Distribution
This is a doctoral-level course designed to provide you a working knowledge of the most common techniques and approaches to multivariate statistics. The goal of the course is to develop your ability to conduct and interpret quantitative research, focusing more on the application and a little less on the theory or math behind how the analysis in conducted. By the end of the course, students will know how to 1) introduce different methods of multivariate data analysis, 2) explain how to select an appropriate technique, given a research objective, 3) understand a given techniques assumptions, and interpret results, 4) produce basic visualizations and tables from SPSS suitable for publishing, and 5) use SPSS to conduct multivariate analysis. It is assumed that students have been exposed to the fundamentals of statistics and that they have some knowledge of the statistical techniques covered.
Instructor: Gary McGill
Research Interests: Federal Income Tax, International Tax, Accounting for Income Taxes, Real Estate, and Urban Economics
This course reviews scholarly accounting research. Such research encompasses several topics and uses various methods. Topics addressed include the use of accounting information by investors and other stakeholders, the role of accounting in capital markets, accounting standard setting, the use of accounting information in budgeting, compensation, and governance, the role of auditing, and tax compliance and tax policy. Methods include analysis of archival data, behavioral experiments, surveys, and analytical modeling.
Instructor: Aaron Hill
This course is designed to help you gain an understanding of a). The process of publishing research that contributes to theory and practice; b). Practical considerations associated with a career in academia; and c). Exposure to a limited number of theories pertaining to strategic leadership and governance as they apply to public, private, and start-up organizations. This course is for doctoral students interested in pursuing scholarship in business domains. As a course early in the doctoral course sequence, this course aims to provide an intellectual foundation that students can build upon in subsequent courses and intellectual pursuits. Importantly, this course will draw on insights from strategic leadership and governance as the theories and related practical topics in this research domain apply to a range of research domains and practical topics; thus, the processes of discovery with respect to theoretical and practical insights developed in this course, albeit with specific adjustments for domains and publication outlets, will apply to research in related domains (e.g., accounting, entrepreneurship, finance, information systems, marketing, organizational behavior, and strategic management more broadly, among other allied fields). The reading list is not meant to be exhaustive nor to cover every topic of interest and instead, offers a limited view into each topic area that students can broaden as they both gain knowledge and focus their research interests.
Instructor: Gwen Lee
Research Interests: Evolutionary economics, Innovation, Knowledge creation, Industry evolution, Convergence of industry boundaries, Emerging technologies
The objective of the course is to cultivate strategy scholars who can synthesize and translate theoretical and empirical research in an authoritative evidential manner that makes these findings accessible for academic readers as well as existing and future “thought leaders.”
The course content focuses on value creation, value appropriation, and value innovation. The course participants will develop: (a) Reviews of what we already know; (b) Integration of diverse theories and empirical findings that inform in a new and interesting way; (c) Forward-looking expositions that integrate and articulate existing theory and findings with new and provocative ideas; and (d) Integration of theory and research in management with related advances in other non-management sciences and disciplines.
Course projects may include: (a) Important and interesting replications/extensions of prior findings that significantly change our understanding of an issue or its boundary conditions; (b) Timely evidence about phenomena that have or may have implications for public policy or managerial practice, (e.g., regarding the effects of economic conditions, corporate governance, contemporary management practices, changing employment conditions); (c) Evidence that informs major scholarly debates in the field of management and organizations; (d) New evidence-based assessments of managerial and organizational interventions.
In lieu of a comprehensive exam on content, students have to meet the research-related standards reflected in the following three-stage process:
End of second fall term in program:
At the end of the second fall term of the program, DBA students will submit a paper that identifies an important research question, provides a review of the relevant literature surrounding that question, and puts together a project plan for investigating that question (i.e., a plan explaining exactly how any initial qualitative or quantitative research would be performed). Based on the focus of this proposal, the student will identify or be assigned a faculty advisor, in consultation with the student, as they move into their second year. The faculty advisor will provide feedback on the project plan and interact with the student during the second year as they work on the project plan implementation.
Based on a positive assessment by their faculty advisor, at least a two-member doctoral committee, including their faculty advisor, will be formed by the student. The committee can include one outside member, such as emeriti faculty or those from other colleges or institutions.
End of summer term of second year in program:
By the end of the summer term of the second year, the student will submit a dissertation proposal defense. An assessment of the paper by the faculty advisor will serve as the doctoral student qualifier.
Fall term of the third year in program:
By the end of the fall semester of the third year, the student will submit a final project proposal to the doctoral committee and defend it orally. The proposal will be a detailed description of the next stage of research to be completed on the topic. It should include the aspects of the research question they will be investigating beyond what was done in the initial paper, and a methodology to guide the next stage of the research. Another possibility is that the student could be striking out in an entirely different direction than the paper submitted in spring. In this instance, they would be submitting and defending an appropriate literature review and research plan for the new research question.
In addition to assessing the proposal itself, the oral proposal defense will ensure the student’s mastery of the relevant literature surrounding the project topic. If the proposal defense is passed, the student becomes a DBA candidate, qualified to complete their dissertation and earn the degree. Any student who fails the oral examination will be permitted to re-take it one time.
The dissertation will be supervised by a committee consisting of at least two graduate faculty members. At least one member will be from the Warrington College of Business and at least one member may be drawn from a different discipline.
- The doctoral committee will be formed no later than the end of the fourth term.
- By the end of the seventh term, the student must make a formal presentation of the dissertation proposal to the supervisory committee.
- Once the proposal is approved by the committee, the student works under the committee’s supervision to complete the dissertation and sit for the final oral defense at the end of the ninth term.
- In the event that a project cannot be completed in term 9 and the student receives an incomplete grade, the student will continue working with the doctoral committee and will have to register for at least three credits (if summer, two credits) in each term he/she does not graduate.
Success Stories from the DBA Program
In the final year of the program, third-year students will attend a two-day program, which provides a variety of presentations by DBA alumni. Presenters provide ‘after graduation’ success stories from the academic arena and beyond.