Business intelligence as a discipline requires data collection, integration, analysis, and visualization as the basis for advanced decision making in businesses and organizations. This course focuses on the quantitative tools and data used by business intelligence professionals. Students will explore different quantitative research methods and strategies of inquiry, identify and apply those methods and data sets that best address different types of organizational questions, and develop communications techniques to effectively share results with different audiences. Students examine the phases of data exploitation including collection, storage, analysis and visualization using state-of-the-art tools. Ethics, trust, privacy and transparency are all explored to ensure the credibility of the business intelligence results. (3 credits)

This course prepares doctoral students for the applied research process in business intelligence. Students review APA guidelines and look at the special requirements for academic writing. They start thinking of issues in the field that could benefit from additional research and they examine the literature review and writing process to develop their ideas into potential dissertation topics. Students also analyze previous work and consider ethical issues that might occur during the conduct of business intelligence research. (3 credits)

In this course, doctoral students will be exposed to the overall research design process through the analysis of knowledge claims, strategies of inquiry, and the development phases of the research project. The course examines how to consider exogenous factors and how they are applied to the qualitative research methodologies. In addition, this course will provide the student with an introduction to questionnaire design. (3 credits)

In this course, doctoral students will begin to develop their applied research projects in the business intelligence field. This course is designed to provide an increased understanding of the philosophy of science and the nature of scientific thinking. The doctoral students identify researchable topics, the use of existing knowledge to act as a basis for the research project, and the use of information systems to support the identification of known information and data. The course prepares students to be business intelligence leaders who ably use applied research to refine and improve business practices through evidence-based analysis. (3 credits)

In this course, doctoral students will be exposed to the overall research design process through the analysis of knowledge claims, strategies of inquiry, and the development phases of the research project. The course examines how to consider exogenous factors and how they are applied to quantitative, qualitative, and mixed methods research methodologies. In addition, this course will provide the student with an introduction to questionnaire design. (3 credits)

In this course, doctoral students will develop the documentation necessary for approval of their research topic and overall research plan. They will also obtain approval from their dissertation advisor and submit their dissertation proposal for approval by the Faculty Dissertation Committee. Students will evaluate which type of IRB submission is appropriate and prepare the material for its timely submission. The student will also be guided in a thorough literature review on their chosen topic and develop a clear understanding of previous work in their field. (3 credits)

This doctoral course assesses the implications of strategic management in a dynamic environment. Emphasis is placed on the development of action-based strategies for the purpose of creating competitive advantage. Students critically examine leading theories of organizational strategy, as well as the evolution of strategic management, to identify new perspectives applicable to their areas of interest. Course topics include: resource-based competitive advantage, core competency development and expansion, strategic alliances/partnerships, and emerging operational models. (3 credits)

This doctoral course evaluates current and emerging economic issues related to intelligent decision-making in business. Students study how markets work, profit maximization, policy effects and strategic interaction by researching a series of practical economic problems related to the current business environment. The course focuses on central decision problems and the economic analysis needed to guide these business decisions. Students will work on two levels of analysis: 1) the microeconomic approach, which focuses on supply and demand, as well as forces that determine price levels; 2) the macroeconomic level, which takes a top-down approach, looking at the economy as a whole and trying to determine its course and nature. Students also research the impact of government intervention and its effect on intelligent business decision-making. (3 credits)

In this doctoral course, students research and develop an intelligent plan for implementing a major change initiative in a government agency, in a business, or in a not-for-profit. Students research how new initiatives have been launched successfully in different organizations so that they become ingrained in the organization's value proposition. Students design strategy implementation plans for specific organizations to ensure the change initiative will become part of an organization’s identity, allowing for the changing environment in which they must operate. Students review and evaluate modern theories of organizational change in relation to their current organizational context. The role of senior executives as agents of change is explored in detail and approaches to organizational change are researched and evaluated based on their ability to foster growth and sustainable competitive advantage. Specific drivers of change are discussed, including the role of change management in digital transformation.(3 credits)

This doctoral course covers the cybersecurity challenges faced by today's technology-enabled government agencies and businesses. Students explore tools and techniques to manage the risk of a damaging cyber incident, such as a massive data breach. This course examines the business risk posed by cybersecurity challenges due to a constantly changing computing infrastructure. Emphasis is placed on the role of increasing reliance on the Internet, the rise of additional threats posed by cloud computing, mobile computing, large data stores, artificial intelligence, and automated industrial control systems, as well as the risk to essential critical infrastructures such as power generation and distribution. Students evaluate financial and reputation risks by accessing interrelated systems across the global landscape. Additionally, students examine the cost impact of introducing technology and control processes to mitigate these risks as compared to the cost of recovering from a major cyber incident. (3 credits)

This doctoral course covers the effective management of digital transformation efforts taking place throughout government and business organizations to improve operations through the effective use of technology. Students develop a thorough understanding of how to intelligently manage the organizational transformation that occurs when all or parts of an enterprise "go digital." Students develop strategies to best harness the strategic business value of digital technologies by examining the principles behind the design of business models for product and service delivery via digital platforms. The course examines 21st century technologies from the perspective of effective implementation and management of concomitant organizational change. Emphasis is placed on the important relationship between technology and the business, rather than focusing on the technology itself. (3 credits)

This doctoral course covers the application of artificial intelligence (AI) techniques in government and business transformation and examines their important role in business intelligence. Students are first introduced to AI and state-of-the-art tools and techniques for implementing AI, including data science and machine learning technologies. Students will work with existing machine learning models and implement tools to support business intelligence using available data. Existing AI applications in government and industry will be researched and an AI strategy will be developed. Students will evaluate the competitive advantage offered by an effective AI strategy. Students will also examine the trustworthiness of AI, including bias and attack techniques such as data poisoning, and develop an ethical framework and governance strategy for the use of AI techniques. (3 credits)

During this course, the doctoral student will collect and analyze data to further dissertation research developed in previous courses. The purpose of this course is to conduct original applied research which will contribute to the development of the business intelligence field. At the end of the course, the doctoral student must have completed the analysis phase of the dissertation and be ready to focus on the writing process. Students will need to complete a minimum of two (2) semesters of DBA 840 for a total of six (6) credits.