Courses Elective Programme

The courses offered as part of the elective programme may vary from semester to semester. Some of the elective classes are offered on a (rather) regular basis as part of the PhD programme in management by business faculty themselves. Other classes that may count as electives are held at the research Master level at the Departments of Economics, Psychology, or Computer Science at the University.

We therefore want to give you an idea for which type of courses you can expect behind general titles such as "Topics in Human Resources and Organizations 1" or "Topics in Strategy and Innovation 2" and. Please note that these are just exemplary courses - for information on which courses are currently offered or will be offered the upcoming semester please consult u:find or the course directory.

Please note that it is not necessary to complete the Core Programme in order to do courses from the elective programme.  However, it usually makes sense to complete the core programme before finishing all of your elective courses, as some of them will build on the knowledge gained from courses included in the core programme.

Corporate Finance Theory (COT)


The Modigliani Miller theorem (1958) states that, when markets are perfect and complete, financial structure is irrelevant. In this context, corporate finance is just a veil. The specific forms taken by corporate financing don't affect welfare or value. Since that seminal and provocative paper, corporate finance theorists have endeavoured to study how, when Modigliani and Miller's assumptions are relaxed, and the market is assumed to be imperfect and incomplete, financial structure becomes relevant.

Selected References:

• Tirole (2006)

• Biais et al (2013)

• Laffont and Martimort (2002)

Game Theory (GT)

Recommendation for attending this course:

• Successful attendance of the course Management Decision Making, or in-depth knowledge of the contents of that course.


The objective of this course is to learn how to master game theory. Game theory is the theory of making decisions when outcomes are influenced by others making decisions. Games will be played in class to help gain intuition. There will be real life examples (such as auctions, market entry, public good provision) but the main emphasis is on the methodology, the mathematics of strategic decision making. Topics we will be covering include

1. Utility, uncertainty, risk, decision making and rationality

2. Games, strategies and timing

3. Dominance, iterated dominance, rationalizability

4. Extensive form games with perfect information, backwards induction

5. Nash equilibrium

6. Subgame perfection, forwards induction

7. Repeated games, folk theorem

8. Bayesian games

No prerequisites, however if you have never attended a game theory course then you are strongly advised to read some basic material before the course, eg Kokesen & Ok (2007). You will also need to understand decision making under uncertainty and expected utility theory. It is not mandatory, however you are strongly advised to register also for the UE Game Theory 2 tutorial (4 ECTS) held by Mariya Teteryatnikova. These are practice sessions relating to the material of this lecture, and they are extremely useful for learning how to solve the exams of this course (in which you will not be given practice problem sets).

Selected References:

• Kokesen, L. and E. Ok. (2007). An Introduction to Game Theory. Online lecture notes

• Fudenberg, D. and J. Tirole. (1991). Game Theory. MIT Press

• Mas-Colell, A., M.D. Whinston and J.R. Green. (1995). Microeconomic Theory. Oxford University Press (only selected chapters)

Measurement Theory and Scale Development (MTSD)


The course seeks to provide a broad introduction to measurement theory and alternative approaches for developing and assessing multi-item scales. It is aimed at non-experts and the emphasis is on the steps associated with the development and validation of sound measures for use in empirical 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 Multivariate Business Statistics and Structural Equations Modeling courses of the PhD Management program.

The course seeks to familiarize participants with the various stages associated with the construction of sound measures for use in empirical research, highlighting key decisions and potential problems at each stage. Following an introduction of the key concerns of measurement theory, the conceptual underpinnings of alternative measurement perspectives - namely reflective and formative measurement - are discussed. These set the conceptual background for considering operational procedures for developing reflective scales and formative indices and for offering detailed guidelines for measure validation. To enable participants experience measure development 'in action';, the various issues are illustrated with concrete examples of reflective scale development and formative index construction drawn from the literature.

Once participants have become familiar with basic measurement principles, more advanced topics will be addressed such as higher-order models, parceling strategies, and single-item measurement models. Note that in several of the illustrations used, the LISREL program will be applied to estimate the relevant models and, therefore, it is essential that participants are familiar with basic structural equations modeling (SEM) procedures.

The course will take the form of workshop sessions, placing particular emphasis on student participation. Theoretical discussion of key issues will be accompanied by practical demonstration of scale development.

Selected References:

• Netemeyer, R. G.; Bearden, W. O. and Sharma, S. 2003. Scaling Procedures, Sage Publications (ISBN: 0-7619-2027-7)

Strategy Content (SC)

Recommendation for attending this course:

• Successful attendance of the course Theory of Networks, or in-depth knowledge of the contents of that course.


This is a survey course ‐ an introduction to important theories of strategy. A few words about the design principles of this course:

1. Strategy research can be seen as a matrix of theories and phenomena. The structure‐conductperformance
paradigm, game theory, resource based theory, theories of organizational knowledge and
learning, transaction cost economics and evolutionary economics are all widely known and used
theoretical lenses in strategy content research. The phenomena these theories are applied to include
industry concentration, diversification, vertical integration, organization design, knowledge transfer and
management, acquisitions and alliances etc. I have consciously chosen to provide a survey organized by
theories rather than phenomena, given the importance that theory building, testing and development will
play in your future lives as academics (perhaps in the field of strategy).

2. As we typically spend about 1.5 sessions on a theoretical framework, this will hardly make you a master
of that theory. That's what a survey course is about – think of it as a trailer that gives you some sense of
the main attraction. Many of the theories we cover in one session could be the topic of a whole doctoral
seminar by themselves! The reading list for each session will give you additional "below the line"
references to readings that will point the way in terms of deepening your expertise in a particular theory.
You might also consider additional elective coursework as a means to drill deeper into a particular
theory. It is your own responsibility to do follow up work to deepen you knowledge of a theory and its
associated literature.

3. In terms of pre‐requisites, if you have taken some class that covers basic readings in management
theories (e.g. ToN, better FRB) in Vienna (or their equivalents elsewhere), you should be adequately
prepared to take this course. Contact me if you have not taken one or both of these course and are still
keen to take Strategy Content: I will suggest a supplementary reading list.

Selected References:

• Rumelt R.P., Schendel, D.E. & Teece D.J. (1994). Fundamental Issues in Strategy: A research agenda, HBS Press Boston MA, pp. 9‐47, 527‐555.

• Scherer F.M. & Ross D. (1990). "The Structure‐Conduct‐Performance Paradigm". Industrial Market Structure
and Economic Performance, Rand McNally, pp. 1‐7.

• McGahan, A, and Porter, M. (1998). 'How Much Does Industry Matter Really?' Strategic Management Journal, 18 (Summer Special Issue): 15‐30.

• White R. E. (1986). “Generic Business Strategies, Organizational Context and Performance: An Empirical
Investigation”, Strategic Management Journal, 7(3): 217‐231.

• Teece D.J. (1980). “Economies of scope and the scope of the enterprise”, Journal of Economic Behavior &
Organization, 1(3): 223‐247.

• Nelson R. & Winter S. (1982). The Evolutionary Theory of Economic Change Belknap Press of Harvard
University Press, Chapters 2, 4, 5.

• Gibbons R. (2005). “Four formal(izable) theories of the firm?”. Journal of Economic Behavior & Organization, 58(2): 200‐245.

Structural Equations Modeling (SEM)

Recommendation for attending this course:

• Successful attendance of the course Multivariate Business Statistics, or in-depth knowledge of the contents of that course.


This course is targeted to PhD students and seeks to provide a user-friendly introduction to structural equations modeling (SEM) using the LISREL program. It is designed for non-experts and its emphasis is on understanding and applying SEM as a tool in substantive research. The course assumes previous knowledge of data analysis and statistics (including factor analysis and regression).

The course is designed to familiarize participants with the various stages associated with conceptualizing, estimating, and evaluating SEM models, highlighting key decisions and potential problems at each stage. Following an introduction to SEM as an analytical approach, issues associated with the theoretical specification and graphical representation of a SEM model are discussed. These set the background for applying the LISREL program to estimate the model and assess its fit along different criteria. Strategies for model modification and cross-validation are also outlined. To enable participants experience SEM "in action", the above issues are illustrated by using a concrete example of a model specified and estimated with the LISREL program. Detailed guidance for setting up and interpreting the relevant input/output files of the program is also provided.The course will take the form of interactive workshop sessions, placing particular emphasis on student participation.Students are expected to download the (free) student version of the LISREL program from and also read widely on the subject (see Course Text and Additional Reading below).

Selected References:

The required text for the course is:

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

Student should also read the relevant chapters on SEM in:

• Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010): Multivariate Data Analysis, 7th edition, Pearson (ISBN 978-0-13-515309-3).

A selected list of readings on SEM in general and LISREL in particular is given below.

• Anderson, J. C. & Gerbing, D. W. 1988. Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103 (3): 411-423.

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

• Bagozzi, R. P. & Yi, Y. 2012. Specification, Evaluation, and Interpretation of Structural Equation Models. Journal of the Academy of Marketing Science, 40 (1): 8-34.

• Baumgartner, H. & Homburg, C. 1996. Applications of Structural Equation Modeling in Marketing and Consumer Research. A Review. International Journal of Research in Marketing, 13 (2): 139-161.

• Chin, W. W., Peterson, R. A. & Brown, S. P. 2008. Structural Equation Modeling in Marketing: Some Practical Reminders, Journal of Marketing Theory and Practice, 16 (4): 287-298.

• Danes, J. E. & Mann, K. O. 1984. Unidimensional Measurement and Structural Equation Models with Latent Variables. Journal of Business Research, 12 (3): 337-352. [will be available on moodle]

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

• 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.

• Golob, T. F. 2003. Structural Equation Modeling for Travel Behavior Research. Transportation Research Part B: Methodological, 37 (1): 1-25.

• Iacobucci, D. 2009. Everything You Always Wanted to Know about SEM (Structural Equations Modeling) but were Afraid to Ask. Journal of Consumer Psychology, 19 (4): 673-680.

• Iacobucci, D. 2010. Structural Equations Modeling: Fit Indices, Sample Size, and Advanced Topics. Journal of Consumer Psychology, 20 (1): 90-98.

• MacCallum, R. C. & Austin, J. T. 2000. Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology, 51 (1): 201-226.

• Mackenzie, S. B. 2001. Opportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling. Journal of Consumer Research, 28 (1): 159-166.

• Reisinger, Y. & Turner, L. 1999. Structural Equation Modelling with LISREL: Application to Tourism. Tourism Management, 20 (1): 71-88.

• Schreiber, J. B., Stage, F. K., King, J., Vora, A. & Barlow, E. A. 2006. Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Education Research, 99 (6): 323-338.

• Shah, R. & Goldstein, S. M. 2006. Use of Structural Equation Models in Operations Management Research: Looking Back and Forward. Journal of Operations Management, 24 (2): 148-169.

• Shook, C. L., Ketchen, D. J., Hult, G. T. M. & Kacmar, M. 2004. An Assessment of the Use of Structural Equation Modeling in Strategic Management Research. Strategic Management Journal, 25 (4): 397-404.

• Steenkamp, J. B. E. M. & Baumgartner, H. 2000. On the Use of Structural Equation Models for Marketing Modeling. International Journal of Research in Marketing, 17 (2-3): 195-202.

• Tomarken, A. J. & Waller, N. G. 2005. Structural Equation Modeling: Strengths, Limitations, and Misconceptions. Annual Review of Clinical Psychology, 1: 31-65.

• Williams, L. J., Edwards, J. R. & Vandenberg, R. J. 2003. Recent Advances in Causal Modeling Methods for Organizational and Management Research. Journal of Management, 29 (6): 903-936.

Theory of Networks (ToN)


The course provides a discussion of the theoretical foundation of networks (strategic alliances, joint ventures, franchising, licensing, consortia, clusters, virtual networks). It emphasizes the relationships between different theories and networks. The sessions provide an overview of a number of the major theoretical and methodological approaches adopted in network research as it evolved into a specific research field. The course incorporates sessions on essential aspects of network research including transaction cost economics, property rights theory, information economics, resource-based theory, real options reasoning and the relational view of networks. In particular, the course highlights current research challenges and methodological issues facing the research in economics and management of networks and encourages a discussion among the participants to determine what constitutes an appropriate future research strategy, especially applied to your PhD-project.

Selected References:

• Geyskens et al. (2006). Make, Buy or Ally, Academy of Management Journal, 49, 519 – 43.

• Mayer, K.J. and Salomon, R.M. (2006). ‘Capabilities, contractual hazards, and governance: Integrating resource-based and transaction cost perspective’. Academy of Management Journal, 49, 942-959.

• Akerlof GA. (1970). The market for 'lemons': quality uncertainty and the market mechanism. Quarterly Journal of Economics 84: 488-500.

• Reuer JJ, Ragozzino R. (2006). Agency hazards and alliance portfolios, Strategic Management Journal, 27, 27 – 43.

• Zaheer, A. and Venkatraman, N. (1995). ‘Relational governance as an interorganizational strategy: an empirical test of the role of trust in economic exchange’. Strategic Management Journal, 16, 373–92.

• Lazzarini, S. G., G. J. Miller, T. R. Zenger (2008). Dealing with the Paradox of Embeddedness: The Role of Contract and Trust in Facilitating Movement out of Committed Relationship, Organization Science, 19, 709 – 728.