COURSE UNIT TITLE

: DECISION SUPPORT SYSTEM DESIGN AND IMPLEMENTATION

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DBA 6138 DECISION SUPPORT SYSTEM DESIGN AND IMPLEMENTATION ELECTIVE 3 0 0 9

Offered By

Business Administration (English)

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR SABRI ERDEM

Offered to

Business Administration (English)

Course Objective

This course aims to expertise the students knowledge of decision support systems, advanced concepts of decision support systems and the advances of system developing approaches.

Learning Outcomes of the Course Unit

1   Demonstrate expertise of the advance issues of decision support systems
2   Demonstrate understanding of the advanced concepts of designing a new system,
3   Build advanced decision making applications for a business sub system,
4   Apply and expertise in executive support system design

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction
2 Understanding Requirements User Needs, Business Needs, System Requirements
3 Transforming Requirements into Design
4 Designing User Interfaces and Dashboards HTML, CSS Applications
5 Responsive Design and Mobile Computing Bootstrap Applications
6 Prototyping Rapid, Functional and Non-Functional Computer Applications: Ruby On Rails
7 Technological Infrastructure for Decision Support Systems SaaS, PaaS, IaaS, DaaS Computer Applications: Ruby On Rails
8 Machine Learning and Data Mining OLAP Cubes, Flexible Report Generation Computer Applications: Ruby On Rails
9 Intelligent Agents and Decision Support Systems Computer Applications: Ruby On Rails
10 Big Data Computer Applications: Ruby On Rails
11 Testing, Verification and Validation Computer Applications: Ruby On Rails
12 Term Project Presentations Computer Applications: Ruby On Rails
13 Term Project Presentations Computer Applications: Ruby On Rails

Recomended or Required Reading

Textbook:
Sauter, V. L. (2014). Decision Support Systems for business intelligence. John Wiley & Sons.

Articles:
Grosswiele, L., Röglinger, M., & Friedl, B. (2013). A decision framework for the consolidation of performance measurement systems. Decision Support Systems, 54(2), 1016-1029.
Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52(2), 450-463.
March, S. T., & Hevner, A. R. (2007). Integrated decision support systems: A data warehousing perspective. Decision Support Systems, 43(3), 1031-1043.
Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67-80.
Desanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group decision support systems. Management science, 33(5), 589-609.
Ives, B., & Olson, M. H. (1984). User involvement and MIS success: a review of research. Management science, 30(5), 586-603.
Byun, H. S., & Lee, K. H. (2005). A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method. The International Journal of Advanced Manufacturing Technology, 26(11-12), 1338-1347.
Sauerwein, E., Bailom, F., Matzler, K., & Hinterhuber, H. H. (1996, February). The Kano model: How to delight your customers. In International Working Seminar on Production Economics (Vol. 1, pp. 313-327).
Sprague Jr, R. H. (1980). A framework for the development of decision support systems. MIS quarterly, 1-26.
Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology.Decision support systems, 33(2), 111-126.
Bui, T., & Lee, J. (1999). An agent-based framework for building decision support systems. Decision Support Systems, 25(3), 225-237.

Online Resources:

Codecademy: http://www.codecademy.com/learn
Ruby On Rails Tutorial: http://guides.rubyonrails.org/getting_started.html

Planned Learning Activities and Teaching Methods

Lecture, group work, presentations, class discussions, field study

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.20 + FIN* 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + RST* 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. Midterm and Final Exam
Students will be assessed on their knowledge of concepts and theories through an essay-type written exam semester
2. Term Project
Groups will do an observation in a real business environment about intelligent systems and prepare a written report based on the format given by the instructor. They are expected to share their observation with their class-mates through oral presentations.
3. Class Discussions and Presentation
Students will be given certain cases or questions related to the concepts covered in the class. Groups will debate on the topics and present their opinions. Students are expected to contribute to class discussions.

Language of Instruction

English

Course Policies and Rules

1. Attending at least 70 percent of lectures is mandatory.
2. Plagiarism of any type will result in disciplinary action.
3. Students are expected to participate actively in class discussions.
4. Students are expected to attend to classes on time.
5. Students are expected to prepare ahead of time for class.

Contact Details for the Lecturer(s)

sabri.erdem@deu.edu.tr

Office Hours

TBA

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 11 3 33
Preparations before/after weekly lectures 11 5 55
Preparation for final exam 1 30 30
Preparation for midterm exam 1 30 30
Preparing assignments 1 40 40
Preparing presentations 2 10 20
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 214

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.1155455
LO.2155411
LO.3155455
LO.4155455

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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