Betriebswirtschaft

Bewerben Studienberatung
Business Department

Module manual

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Decision Making

Prerequisite

General understanding of business administration, sound knowledge of business mathematics (especially linear optimization) and statistics (especially probability theory and inductive statistics)
Teaching methods Lecture
Learning target / Competences

Students are familiar with the phases of an ideal-typical decision-making process. They understand the requirements of the sub-decisions to be made in each of these phases. On this basis, they are not only able to apply the methodological rules provided by decision theory to support making these sub-decisions, but also to apply quantitative methods such as multivariate analysis methods, forecasting methods or operations-research methods to deal with a given decision.
Students identify the models and solution methods appropriate for a given problem, select suitable models and methods, and formulate conceptual specifications for suitable adaptations. Depending on the topic area, they implement this using application software. They are able to weigh the increase in decision quality against the additional costs (time, money) associated with the application of methodological support rules. Depending on the importance of the decision, students form their own opinion about the extent to which support rules should be applied to balance decision quality and decision costs. In particular, they are able to critically evaluate quantitative methods and their results. They act as contacts in the selection of appropriate quantitative methods when dealing with new decision in the company.
Duration 1 Semester
Hours per week 4.0
Overview
Classes 60 h
Individual / Group work: 120 h
Workload 180 h
ECTS 6.0
Requirements for awarding credit points

Module assessment: Written exam (K120)
Responsible person

Prof. Dr. rer. pol. habil. Matthias Graumann
Recommended semester 1. oder 2. Semester
Frequency Annually (ws)
Usability

Betriebswirtschaft (Master)
Lectures

Decision Making

Type Lecture
Nr. W1153
Hours per week 2.0
Content

Everybody makes numerous decisions each and every day. Many of them are of minor importance, but some decisions require serious consideration. The course will teach students how to tackle these decisions. The concept is called Rational Decision Making. It is based on a model of a decision making process with seven phases. The course will highlight each and every phase and will then proceed with case studies. Thus, students will have the opportunity to apply their new knowledge to cases of practical decision making.

By the end of the course the students will have understood the concept of procedural rationality. They should be able to pass consciously through the phases of a decision making process while making use of the methodological recommendations of decision analysis.
Literature

Edwards, W./Miles Jr. R.F./v. Winterfeld, D. (ed.): Advances in Decision Analysis, Cambridge et al., 2007.
Eisenführ, F./ Weber, M./Langer, T.: Rationales Entscheiden, 5. ed., Berlin et al. 2010 (English version: Eisenführ, F./Weber, M./Langer, T.: Rational Decision Making. Berlin et al. 2010).
Forbes, D.P.: Reconsidering the Strategic Implications of Decision Comprehensiveness. In: Academy of Management Review, Vol. 32 (2007), p. 361-376.
Graumann, M.: Der Entscheidungsbegriff in § 93 Abs. 1 Satz 2 AktG - Rekonstruktion des traditionellen Verständnisses und Vorschlag für eine moderne Konzeption. In: Zeitschrift für Unternehmens- und Gesellschaftsrecht (ZGR), Band 40 (2011), Heft 3, p. 293-303.
Graumann, M./Engelsleben, T.: Warum Geschäftsleiter für die Beurteilung der Informationsgrundlage von Prognosen ein regelbasiertes Verfahren benötigen. In: Zeitschrift für Corporate Governance (ZCG), 6. Jg. (2011), p. 69-75.
Graumann, M./Grundei, J.: Wann entsprechen unternehmerische Entscheidungen der gesellschaftsrechtlichen Anforderung ”angemessener Information”? In: Die Betriebswirtschaft (DBW), 71. Jg. (2011), p. 379-399.
Graumann, M./Linderhaus, H./Grundei, J.: Wann ist die Risikobereitschaft bei unternehmerischen Entscheidungen ”in unzulässiger Weise überspannt”? In: Betriebswirtschaftliche Forschung und Praxis, 61. Jg. (2009), p. 492-505.
Graumann, M./Niedostadek, A.: Bestimmung und Beurteilung von Entscheidungsrisiken. In: Der Aufsichtsrat, 7. Jg. (2010), p. 174-176.
Grundei, J./Graumann, M.: Beurteilung der Qualität von Managemententscheidungen durch den Aufsichtsrat. In: Der Aufsichtsrat, 6. Jg. (2009), p. 53-55.
Kahnemann, D./Lovallo, D./Sibony, O.: Grundsätze für Entscheider. In: Harvard Business Manager (2011), p. 19-31.
Keeney, R.L.: Value-Focused Thinking. Cambridge et al. 1996.
Keeney, R.L.: Evaluation of Proposed Storage Sites. In: Operations Research, Vol. 27 (1979), p. 48-64.
Keeney, RL./Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York u.a. 1976.
Watson, St.R./Buede, D.M.: Decision Synthesis. The Principles and Practice of Decision Analysis. Cambridge u.a. 1987.
Weber, M.: Nutzwertanalyse. In: Erich Frese (ed.), Handwörterbuch der Organisation, 3. ed., Stuttgart 1992, Sp. 1435-1448.
Winterfeldt, D. v./Edwards, W.: Decision Analysis and Behavioral Research. Cambridge 1986.

Data Analysis Methods

Type Lecture
Nr. W1181
Hours per week 2.0
Content

Selected quantitative methods of data analysis from one or more of the following topics:

  • Business analytics
  • Predictive analytics (e.g. neural networks, decision trees, logistic regression)
  • Multivariate analysis (e.g. analysis of variance, discriminant analysis, conjoint measurement, structural equation models, cluster analysis, factor analysis)
  • Advanced statistical methods (e.g., forecasting methods, stratified sampling, advanced testing methods)
Literature

Backhaus, K.; Erichson, B.; Plinke, W.; Weiber R.: Multivariate Analysemethoden: eine anwendungsorientierte Einführung. 15. ed. Springer, 2018.
Seiter, M.: Business Analytics. Effektive Nutzung fortschrittlicher Algorithmen in der Unternehmenssteuerung. Verlag Franz Vahlen, Munich 2017.
Cleve, J; Lämmel, U.: Data Mining. De Gruyter Oldenbourg. 2. ed. 2016.
Backhaus, K.; Erichson, B.; Weiber R.: Fortgeschrittene multivariate Analysemethoden: eine anwendungsorientierte Einführung. 3. ed. Springer Gabler 2015.
Larose, D. T./Larose, C. D. (2015): Data Mining and Predictive Analytics. Wiley Series on Methods and Applications in Data Mining, 2nd edition. Hoboken, New Jersey 2015.
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