DECISION SUPPORT SYSTEMS

Course Description

This course provides a rigorous exploration of Decision Support Systems (DSS), integrating theoretical foundations with advanced computational techniques to support complex managerial decision-making. Students examine DSS architectures, data-driven and model-driven approaches, and the integration of human cognition into system design. The course covers mathematical modeling, optimization, simulation, and multi-criteria decision analysis, as well as emerging areas such as AI-enabled DSS, machine learning, neural networks, and big data analytics. Practical applications are explored through data warehousing, OLAP, business intelligence, and cloud-based decision platforms. Emphasis is placed on research, critical evaluation, and hands-on system development, enabling students to design, analyze, and critique intelligent DSS for strategic and operational contexts.

DECISION SUPPORT SYSTEMS

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

No weekly content has been defined yet.

Reference Books & Course Materials

No reference books have been listed.

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

1. Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
2. Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
3. Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
4. Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
5. Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
6. Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
7. Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
8. Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
9. Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
10. Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
11. Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
12. Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.

Po-Lo Matrix

The PO-LO matrix has not been populated yet.