Core curriculum PhD in Management

The core curriculum (binding for all PhD students across the different management specializations) comprises five courses. Students may select from the following range of classes that are offered on a regular basis by our management faculty:


Advanced Optimization (AO)

Prerequisites for attendance:

• Enrollment in the PhD program of management

Contents:

The class gives an introduction into the basic principles of dynamic optimization, nonlinear optimization, and optimal control theory. We first cover the Bellmann Principle and the Kuhn Tucker condition. We then proceed to analyze differential games. Alternatively, the course covers use of methods in physics for optimization. Examples include Lagrangian methods, methods of statistical mechanics for stochastic optimization. We also discuss the application of quantum physics and quantum computing. Students are requested to solve exercises in advance and to present it in class.

Selected References:

• Feichtinger, G., & Hartl, R. F. (1986). Optimale Kontrolle ökonomischer Prozesse: Anwendungen des Maximumprinzips in den Wirtschaftswissenschaften. Berlin: W. de Gruyter.

• Grass, D. (2008). Optimal Control of Nonlinear Processes. Springer-Verlag.

• Hillier, F. S., & Lieberman, G. J. (1980). Introduction to operations research. San Francisco: Holden-Day.

• Nemhauser, G. L., & Wolsey, L. A. (1988). Integer and combinatorial optimization. New York: Wiley.

Experimental and Simulation Methods (ESM)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of the courses Management Decision Making and Multivariate Business Statistics

Contents:

This course gives an overview of simulation methods (first two sessions). Then, each student will discuss a scientific paper on a specific simulation approach with application to a certain field (e.g., marketing, organization, production, logistics, innovation and technology management, life sciences, health care). Students can choose from preselected papers. As students are highly encouraged to select a field in relation to their PhD-project, they might also choose another more relevant paper for them (third session). All participants will then outline ideas how to best implement this approach by anylogic (fourth session), and will finally present the implementation of the approach in anylogic (fifth session).

Management Control (MC)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of the courses Management Decision Making and Advanced Optimization

Contents:

The course introduces contract theory as a methodological tool for analyzing personnel, organizational, financial and public economics problems. The ultimate course goal is twofold:

a) generally, students achieve an understanding of contract theory as modeling tool within their fields of application;

b) specifically, interested students learn the basic framework to develop their own contractual models and/or, respectively, empirical investigations of contract theoretical models.

Selected References:

• Bolton, P. und M. Dewatripont (2005). Contract Theory, Cambridge, MA, and London, UK.

• Laffont, Jean-Jacques and David Martimort (2002). The Theory of Incentives: the principal-agent model, Princeton, NJ, and Woodstock, UK.

• Salanie, B. (1997). The Economics of Contracts: A Primer, Cambridge, MA, and London, UK.

Management Decision Making (MDM)

Prerequisites for attendance:

• Enrollment in the PhD program of management

Contents:

The course covers main areas of decision theory at an advanced level. First, we analyze how preferences can be modeled and how multidimensional evaluation is related to dominance and efficiency. We use the expected utility theory for decisions under risk and consider applications and extensions to the concept. Then we look at the value of information. As the last part of the lecture, we cover multi-criteria decisions.

Selected References:

• Hadar, J. and W. R. Russell (1969). Rules for Ordering Uncertain Prospects. American Economic Review 59(1): 25-34.

• Hershey, J. C. and P. J. H. Schoemaker (1985). Probability versus certainty equivalence methods in utility measurement: Are they equivalent? Management Science 31(10): 1213-1231.

• Kahneman, D. and A. Tversky (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica 47 263-291.

• Roy, B. (1996). Multicriteria Methodology for Decision-Aiding. Kluwer Academic Publishers, Dordrecht.

• Saaty, T. L. (1980). The Analytic Hierarchy Process. New York, McGraw-Hill.

• Starmer, C. (2000). Developments in Non-Expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk. Journal of Economic Literature 38(2): 332-382.

• Tversky, A. and D. Kahneman (1986). Rational Choice and the Framing of Decisions. Journal of Business 59(4): 251-278.

• Wickham, P. A. (2003). The representativeness heuristic in judgements involving entrepreneurial success and failure. Management Decision 41(1/2): 156-167.

Multivariate Business Statistics (MBS)

Prerequisites for attendance:

• Enrollment in the PhD program of management

Contents:

The course consists of three parts. In the first part, we will cover the theoretical principles of selected multivariate techniques. Students will be expected to complement these classes through individual literature studies of text book material, and they will sit a written test covering the sessions of this first part of the class. In the second part of the course, students will present an empirical article, and they will be encouraged to discuss the techniques used by the authors of that paper (mid-term presentations). Finally, in part three of the class, students will conduct a practical data analysis project with a given data set. For data analysis, we deploy the PASW (SPSS).

Selected References:

• Backhaus, K. et al. (2011). Multivariate Analysemethoden, 13. Aufl., Springer.

• Hair, J.F.Jr., Black, W.C., Babin, B.J., Anderson, R.E. (2010): Multivariate Data Analysis, 7th ed., Prentice Hall.

• Iacobucci, D., Churchill, G.A.Jr., (2010). Marketing Research – Method. Foundations, 10th ed., South-Western.

• Tabachnik, B.G., Fidell, L.S. (2007). Using Multivariate Statistics, 5th ed., Pearson/Allyn&Bacon.

Philosophy of Science (PoS)

Prerequisites for attendance:

• Enrollment in the PhD program of management

Contents:

This course gives both a theoretical overview on the foundations of philosophy of science and a hands-on introduction to practicing science and knowledge creation. In the first part we explore what science is, what its goals are, what it does, how it works, and what its basic assumptions are about knowledge, methods, the world, etc. We take a closer look at the processes involved in developing scientific knowledge/models; we follow the path from the phenomenon of interest, via the processes of observation, measuring, interpreting data, applying statistical methods, forming hypotheses, constructing scientific models/theories, making predictions and experimental designs, to finally "manipulating" the phenomenon of interest in an experiment (or simulation). We reflect on these processes from the perspective of students’ areas of specialization and taking into account their research questions.

Structural Equation Modeling (SEM)

Prerequisites for attendance:

• Enrollment in the PhD program of management

• Successful attendance of the courses Management Decision Making and Advanced Optimization

Contents:

The course seeks to provide a user‐friendly introduction to structural equations modeling (SEM) using the LISREL program. It is designed for non‐experts and the emphasis is on understanding and applying SEM as a tool in substantive research. The course is designed for PhD students and assumes previous knowledge of data analysis and statistics (including factor analysis and regression). Students taking this course must have already successfully completed the Management Decision Making and Multivariate Business Statistics courses of the PhD Management core program.

Selected References:

• Bagozzi, R. P. & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1): 74‐94.

• Bollen, K. A. & Lennox, R. (1991). Conventional Wisdom on Measurement: A Structural Equation Perspective. Psychological Bulletin, 110 (2): 305‐314.

• Churchill, G. A. (1979). A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research, 16: 64‐73.

• Diamantopoulos, A. & Winklhofer, H. (2001). Index Construction with Formative Indicators: An Alternative to Scale Development. Journal of Marketing Research, 38 (2): 269‐277.

• Diamantopoulos, A. and Siguaw, J.A. (2000). Introducing LISREL, Sage Publications (ISBN 0‐7619‐5171‐7).

• Gefen, D., Straub, D. W. & Boudreau, M‐C. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, 4 (7): 1‐79.

• Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate Data Analysis, 7th edition, Pearson.

• Medsker, G. J., Williams, L. J. & Holahan, P. J. (1994). A Review of Current Practices for Evaluating Causal Models in Organizational Behavior and Human Resources Management Research. Journal of Management, 20 (2): 439‐464.