Institute of Information Systems and Marketing (IISM)

Business Administration in Information Engineering and Management

  • type: Vorlesung (V)
  • semester: SS 2021
  • time: 2020-04-24
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude


    2020-05-08
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-05-15
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-05-22
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-05-29
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-06-05
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-06-12
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-06-19
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-06-26
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-07-03
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-07-10
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-07-17
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude

    2020-07-24
    09:45 - 11:15 wöchentlich
    10.91 Redtenbacher-Hörsaal
    10.91 Maschinenbau, Altes Maschinenbaugebäude


  • lecturer: Prof. Dr. Andreas Geyer-Schulz
  • sws: 2
  • lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/dABYHXBKSkWpiAKs3j9dzQ">2540500</a>
Prerequisites

Basic knowledge from Operations Research (linear programming) and from decision theory are expected.

Bibliography
  • G. Bamberg und A. G. Coenenberg (2006). Betriebswirtschaftliche Entscheidungslehre. (13. edition), chapter 1 - 8, pages 1 - 270.
  • Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach The Intelligent Agent Book. Prentice-Hall, Upper Saddle River. chapter 2, pages 31 - 37.
  • Porter, M. E. (1998a). Competitive Advantage: Creating and Sustaining Superior Performance. The Free Press, New York, 2 edition. chapter 1, pages 1 - 30
  • Porter, M. E. (1998b). Competitive Strategy: Techniques for Analyzing Industries and Competitors. The Free Press, New York, 2 edition. chapters 1+2, pages 1 - 46
  • Horngren, C. T., Datar, S. M., and Foster, G. (2003). Cost Accounting: A Managerial Emphasis. Prentice-Hall, Upper Saddle River, 11 edition. chapter 13, pages 446 - 460
  • Cooper,W.W., Seiford, L. M., and Tone, K. (2000). Data Envelopment Analysis. Kluwer Academic Publishers, Boston. chapter 2, pages 21- 25
  • Copeland, T. and Weston, F. (1988). Financial Theory and Corporate Policy. Addison-Wesley, Reading, 3 edition. pages 18 - 41 and chapter 4.E, pages 92 - 95].
  • Myerson, R. B. (1997). Game Theory. Harvard University Press, London, 3 edition. pages 99-105.
  • Milgrom, P. and Roberts, J. (1992). Economics, Organization and Management. Prentice Hill [Chapter 2, pp. 25-39].
Content of teaching

In this lecture, classical Business Administration is applied to businesses in an information- and communicationtechnological environment. The process to extract relevant data for decision making from operational accounting systems receives special attention. In order to do so, topics such as activity-based costing and transaction costs models are addressed. The automization of the decision making process in businesses by data bases is another focus of the module. To solve such issues within a company, relevant methods such as decision theory and game theory are lectured. Finally, complex business relevant questions in a dynamically changing environment are adressed by presenting models and methods from system dynamics.

Workload

The total workload for this course is approximately 150 hours (5 credits):

Time of attendance

  • Attending the lecture: 15 x 90min = 22h 30m
  • Attending the exercise classes: 7 x 90min = 10h 30m
  • Examination: 1h 00m

Self-study

  • Preparation and wrap-up of the lecture: 15 x 180min = 45h 00m
  • Preparing the exercises: 40h 00m
  • Preparation of the examination: 31h 00m

Sum: 150h 00m

Aim

The student is able to

  • transfer models from Business Administration to situations in business whose basic conditions are changed due to the implementation of information and communication technology,
  • apply methods from Business Administration (Decision theory, game theory, operations research, etc.) to questions of Information Engineering and Management,
  • analyze the potential to automize the decision making process in businesses by data bases,
  • describe the process to extract relevant data for decision making from operational accounting systems.
Exam description

Assessment consists of a written exam of 1 hour length following §4 (2), 1 of the examination regulation and by submitting written papers as part of the exercise following §4 (2), 3 of the examination regulation.

The course is considered successfully taken, if at least 50 out of 100 points are acquired in the written exam. In this case, all additional points (up to 10) from excersise work will be added.

Grade: Minimum points

  • 1,0: 95
  • 1,3: 90
  • 1,7: 85
  • 2,0: 80
  • 2,3: 75
  • 2,7: 70
  • 3,0: 65
  • 3,3: 60
  • 3,7: 55
  • 4,0: 50
  • 5,0: 0

The grade consists of approximately 91% of exam points and 9% of exercise points.