Information Systems Engineering and Management Ph.D.
Doctorate education focuses on enabling the student to make original contributions to their respective fields of study.
The Information Systems Engineering and Management Ph.D. Program creates information systems thinkers and leaders with the ability to add to the body of knowledge and practice in today’s and tomorrow’s academic, public, and private organizations.
There are two phases of the doctoral program at HU: an initial learning phase that can include coursework, seminars, research, and fieldwork that contributes to the student’s knowledge in the program of study; and a second research phase that focuses on the student’s original research culminating in his/her final dissertation defense. Upon a student’s defense of his/her dissertation as well as completion of all other milestones and graduate requirements, the student will be awarded the doctoral degree in his/her program of study.
The Information Systems Engineering and Management program will produce Ph.D. graduates who:
- Create original knowledge and understanding by contributing new theories, concepts, or new applications of existing theories to a discipline or area of professional practice;
- Advance research efforts and disseminate results to peers and the community including conferences, journal outlets, and/or internal organizational formats; and,
- Integrate theories and concepts through critical reflection, synthesis, and interpretation for solutions of private, public, national, or global problems.
Doctorate Program Admissions Process
The Information Systems Engineering and Management (ISEM) area of the Harrisburg University of Science and Technology is seeking applicants for the Ph.D. program in ISEM that can add to the diverse backgrounds and research interests of its current students. Ideal applicants should have a master’s degree in Information Systems Engineering, Information Systems, or a closely related field of study with prior coursework or a strong background in the following areas:
- Business strategy and management
- Information systems planning
- Artificial intelligence
- Systems analysis and design
- Enterprise architecture and systems integration
- Research and writing (leading to the production of a thesis or a scholarly publication)
A faculty admission committee will evaluate each applicant’s candidacy once all admissions materials have been received. The following are requirements in addition to those that are part of the general doctoral admissions requirements:
- GRE score in the 65th percentile or above in the Quantitative portion
- A minimum master’s degree GPA of 3.30 (GPA 3.50+ preferred)
This program requires a total of 36-48 semester hours: 9-21 semester hours of graduate and doctoral Breadth courses (listed below), 9 semester hours of doctoral Depth courses, 6 semester hours of Doctoral Research Seminar, and 12 semester hours of the dissertation process. The semester hour value of each course appears in parentheses ( ).
Interdisciplinary presentation of artificial intelligence as a coherent body of knowledge to acquaint the student with the key concepts and applications in business, science and engineering. The course covers models of intelligent behavior, including problem solving, knowledge representation, reason, planning, decision making, learning, perception, pattern recognition, action, communication and interaction. Recent developments in knowledge management, expert systems, computer-aided consulting and integrated intelligent systems are covered through a wide range of case studies, examples, and hands-on experiments.
This course prepares the student to analyze business information systems in the digital age and to build models and logical designs that can be later implemented. The emphasis will be on the business processes and business requirements needed to build conceptual models that help in analysis of business requirements. This course prepares the student to design complex systems and to build applied designs and architectures.
Modern digital enterprises are characterized by increased automation, mobile services, extended B2B operations with global business partners, and on-demand business services. The main issue in such enterprises is to architect and integrate a very wide range of services quickly and effectively. This course presents a ‘systems’ perspective based on service oriented architecture (SOA) that combines processes, people and technologies, and highlights the role of information and communication technologies, enterprise models, and emerging SOA standards in developing flexible and integrated business architectures.
This course explores a topic or collection of topics of special interest that is timely and in response to critical or emerging topics in the broad field of information systems engineering and management.
Smart Enterprises are the next generation of digital enterprises that heavily rely on artificial intelligence (AI) to deal with customers, suppliers/partners, government agencies and employees. This course highlights advances in research, technologies, systems, and applications as related to intelligent digital enterprises such as smart cities, smart towns, smart healthcare, smart islands, industry4.0, and automated planning environments. The emphasis is on “strategic intelligence” (SI) that refers to the intersection of Business Intelligence, Knowledge Management, and Competitive Intelligence for improving the strategic decision making in Smart Enterprises. Instead of intelligence on one sector, SI concentrates on intelligence that cuts across multiple sectors. The course will use case-based and project-based approaches for discussion and assignments but the focus is on research directions in this broad area of work. Students will be expected to produce a research paper as the final output of this course.
This course will build on the introduction to research methods provided in GRAD 509 to examine and practice advanced methods of research and study design. Topics covered will include research theorizing and model development, instrument development and validation, structural equation modeling, multivariate techniques, grounded theory, action research, multi-methods, and significant study of design science research.
This course will introduce students to the discipline of “Large Scale Systems Engineering”. Also referred to as “Requirements Driven Development” as well as “Systems Engineering”, it represents a disciplined technical and management process by which abstract complex problem descriptions are successfully transformed into fully developed, tested and deployed systems. We will discuss the “art” and “science” of the Large-Scale Systems Engineering discipline. Evolution of Systems Engineering and Advances in Systems Science are discussed. Specialized concepts involved in developing human-engineered complex systems are reinforced primarily through student research and writings. This is a research-focused course that demands extensive student research and academic writing as well as advanced mathematical techniques such as optimization and stochastic processes.
This course provides support to doctoral students within their specific domains of research. Led by the faculty advisor for that domain, the course is designed to provide a forum where faculty and students can come together to discuss, support, and share the experiences of working in research. Research topics in the broad area of information systems engineering and management will be discussed. Topic areas may concentrate on industry sectors (e.g., health, education, manufacturing, transportation, energy, environment, agriculture and others), emerging digital technologies and their impacts on the digital enterprises, and/or latest developments in systems engineering principles such as planning, architectures, integration, engineering/re-engineering, and engineering management. Each topic area will be studied in-depth to educate the students in conducting independent research.
This is an individual study course for doctoral students that culminates in a Ph.D. Thesis. Content to be determined by the student and the student’s Doctoral Committee. May be repeated for credit.
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