Module Catalog “Mannheim Master in Business … of assessment Final exam (90 min., 100%) Preliminary course work - Duration of module 1 semester Offering Fall semester and Spring

Mannheim Master in Business Research “Management” - 1

Module Catalog

“Mannheim Master in Business Research”

(M.Sc.)

Track “Management”

University of Mannheim

(Last update: 08.12.2016)


Index A. Methods .............................................................................................................................. 6

B. Economics ........................................................................................................................ 30

C. Management ..................................................................................................................... 33

D. Master’s Thesis ................................................................................................................ 48


Track “Management“: Curriculum

Semester 1

Field C/E1 Name of Module ECTS

M C CC 501 Decision Analysis 6

ECO2 C CC 502 Applied Econometrics 6

ECO C BE 510 Business Economics I 6

MAN3 /

M E

MAN 630/645/ 646 /

XXX

New Public Management / Introduction to

Entrepreneurship / Leadership and Motivation / HR

Analytics / Method course GESS

3-14

MAN /

M E

MAN 630/ 645/646 /

E 700/ 701/703/

829/863/ 871/880/

882 / MKT 903/

TAX 916 / XXX

New Public Management / Introduction to

Entrepreneurship / Leadership and Motivation / HR

Analytics / Mathematics for Economists / Advanced

Microeconomics / Advanced Econometrics I /

Empirical Industrial Organization / Discrete Choice

and Duration Models / Nonparametric Curve

Estimation / Graduate Public Economics: Empirical

and Quantitative Methods / Stochastic Networks /

Advanced Business Econometrics / Applied

Econometrics / Cross-sectional Analysis /

Multivariate Analysis / Game Theory / Advanced

Quantitative Methods / Longitudinal Data Analysis /

Multilevel Modeling / Modeling Social Processes /

SOEP-Workshop / Bayesian Statistics / Introduction

into R / Meta-Analysis

3-14

Semester 2

Field C/E Name of Module ECTS

M C CC 503 Empirical Methods 6

ECO C BE 511 Business Economics II 6

MAN C MAN 801 Advances in Entrepreneurship and Management

Research

6

MAN C MAN 804 Advances in strategic Management 6

MAN / M E MAN 625/632/

647/648/ 654/655 /

CC 504 / E 700/

701/703/ 829/863/

871/880/ 882 /

MKT 903/ TAX

916 / XXX

Organizational Structures Within an Between Firms

/ Advanced Entrepreneurship / Strategic and

International Human Resource Management /

Incentives and Performance / Corporate

Restructuring / Corporate Strategy: Managing

Business Groups / Corporate Social Responsibility /

Mathematics for Economists / Advanced







3-14

1 Compulsory / Elective 2 Economics 3 Management








Semester 3


M C Crafting Social Sciences Research 6

M C Compact course in Mathematics for Social Scientists 2

MAN C MAN 802 Fundamentals of Non-Profit Management Science 6

MAN C MAN 805 Applied Methods in Management Research 6

MAN C MAN 806 Advances in Organization and Innovation Research 6

MAN C MAN 910 Area Seminar 1

M E E 700/ 701/703/

829/863/ 871/880/

882 / MKT 903/

TAX 916 / XXX














3-14

Semester 4


MAN C MAN 910 Area Seminar 1

Thes4 C MAN 950 Proposal Development 9

Thes C Master Thesis 15

M E E 700/ 701/703/

829/863/ 871/880/

882 / MKT 903/

TAX 916 / XXX














3-14

4 Master’s Thesis


In the “Mannheim Master in Business Research” the following superordinated program-

specific learning goals (LG) will be covered:

LG 1 Profound research capabilities - Students demonstrate an awareness of problems in

their work and are able to independently conduct high-quality scientific research in

their chosen business subject.

LG 2 Proficiency in research methods and analytics - Students are able to profoundly

employ research methods and analytics in their chosen business subject.

LG 3 In-depth subject knowledge - Students have an in-depth knowledge of their chosen

business subject.

LG 4 Academic discourse skills - Students demonstrate their proficiency in academic

discourse skills.


A. Methods

Module: CC 501 Decision Analysis

Contents:

In this course we discuss methods and concepts that were developed to support and improve

rational decision making in various areas of application. We will cover decisions under

certainty and risk (EUT), decisions with single and multiple objectives, and decisions given

incomplete information about probabilities or preferences. The course also covers descriptive

decision theories such as Prospect Theory. An introduction into probability calculus including

Bayes Theorem will be given. We will also discuss various visualization techniques such as

decision trees and decision matrices.

Learning outcomes:

After completing the course students will know about rational decision processes, and how to

structure and visualize decision problems. They will be able to use decision analysis techniques

at an easy level to deal with multiple objectives, risk, intertemporal outcomes and incomplete

information. Moreover they will know about typical behavioural findings that conflict with the

prescriptive methods.

Prerequisites:

Formal: -

Recommended: Every student participating in this course should have completed the 2-

semester finance module of the Mannheim Bachelor program (or equivalent courses). The

lecture generally assumes basic knowledge in mathematics (calculus, optimization) and

statistics (mean, variance, standard deviation).

Obligatory registration: no Further information on registration: -

Courses Hours per week Self-study

Lecture 2 6

Exercise 2 2

ECTS in total 6

Form of assessment Final exam (90 min., 100%)

Preliminary course work -

Duration of module 1 semester

Offering Fall semester and Spring semester

Language English

Program-specific educational goals LG 3

Grade graded

Range of application M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Bus. Inf.,

M.Sc. Bus. Math., M.Sc. MMBR


Module: CC 503 Empirical Methods

Contents:

Theoretical foundations of empirical research. Statistical foundations of empirical research

(key summary statistics, important theoretical probability distributions, methodology of

hypothesis testing and key test statistics). Introduction to qualitative empirical research.

Introduction to experimental empirical research. Introduction to survey-based empirical

research. Introduction to empirical research based on secondary data.

Learning outcomes: By the end of the module:

- participants understand the epistemological possibilities and limitations of empirical

research in the domain of business administration.

- participants are capable to describe a dataset using appropriate summary statistics and test

simple hypotheses about the structure of the data.

- participants know the general approach to conducting qualitative studies, experimental

studies, survey-based studies and studies based on secondary data.

- participants understand fundamental methods for analyzing causal relationships

(particularly ANOVA and analysis of regression). They are able to properly interpret

results form these methods.

Prerequisites:

Formal: -

Recommended: -



Lecture 2 6

Exercise 1 3

ECTS in total 6

Form of assessment Written exam of 90 min.


Lecturer/Person in charge Prof. Dr. Arnd Vomberg

Duration of module 1 Semester


Language German in Fall semester; Spring semester English


Grade graded


M.A: K&W, M.Sc. MMBR


Module: Crafting Social Sciences Research

Contents:

All researchers face similar challenges with core issues of research design. A research design

is a plan that specifies how you are going to carry out a research project and, particularly, how

to use evidence to answer your research question. The goal of this course is to jump-start

students with their dissertation proposal. This course should help students to see the trade-offs

involved in choosing a particular research design in their research proj. Consequently students

are expected to develop own ideas about potential research questions and actively participate

in those seminar-style meetings that are organized within this lecture course.

Learning outcomes:

The goal of this course is to jump-start students with their dissertation proposal. Such a

proposal is a research outline that delineates the doctoral thesis project, including the

motivation for research question(s), the survey of the relevant theoretical and empirical

contributions, the development of a theoretical framework, the specification of the

methodology and planned empirical analysis. You should be prepared to address the following

questions: What makes that an interesting question? Is it an important question? What

contributions would this question and the answers make to the scholarly literature? What

strategies are there to answer your research question(s)?

This course should help students to see the trade-offs involved in choosing a particular research

design in their research projects. Consequently, students are expected to develop own ideas

about potential research questions and actively participate in those seminar-style meetings that

are organized within this lecture course.

Prerequisites:

Formal: -

Recommended: -

Obligatory registration: yes Further information on registration: Website of the CDSS


Lecture 2 10

ECTS in total 6

Form of assessment Draft dissertation proposal (10 pages)


Lecturer/Person in charge Prof. Dr. Thomas Gschwend


Offering Fall semester

Language English


Grade graded

Range of application MMBR, CDSS Doctoral Program


Module: Compact course in Mathematics for Social Scientists

Contents:

In recent decades applications of statistics and formal modelling have become part of the main

stream in political science. Their contribution to our field cannot be overestimated. However,

using these methods may be cumbersome without knowledge of the fundamental math behind.

This course is to provide you with some of these fundamentals, which are beneficiary to your

understanding of formal methods (like game theory) and statistics during your M.A. studies

here at Mannheim. It is therefore recommended to take the course at the beginning of your

M.A.

Learning outcomes:

By the end of the module students know the fundamentals of math behind applications of

statistics and formal modelling.

Prerequisites:

Formal: -

Recommended: -

Obligatory registration: yes Further information on registration: Website of the CDSS


Block Seminar 2 2

ECTS in total 2

Form of assessment Written Exam (120 mins)


Lecturer/Person in charge Martin Kroll



Language English


Grade graded

Range of application MMBR, CDSS Doctoral Program


Module: E 700 Mathematics for Economists

Contents: The course consists of four chapters:

Chapter 1: basic mathematical concepts like sets, functions and relations are introduced and

discussed. Strict mathematical reasoning is explained and applied.

Chapter 2: covers the concept of metric and normed spaces and discusses the convergence of

sequencesin these spaces, the continuity of functions, and the concept of compact sets.

Chapter 3: deal with vector spaces. matrix algebra, linear transformation, and eigenvalues of

matrices.

Chapter 4: covers a multivariate concept of differentiability and its application in solving

unconstraint and constrained optimization problems.

Learning outcomes: The students know basic mathematical concepts of analysis and linear algebra. They can

interpret mathematical formulas that are written in the condensed mathematical syntax. The

students understand the concept of a proof and can develop rigorous mathematical proofs in a

elementary level. They understand abstract mathematical concepts like metric spaces and

linear spaces and are able to comprehend argumentation on basis of abstract mathematical

concepts. They are able to apply their konwoledge; especially they are familiar with the

calculation of limits and derivatives, the methods of linear algebra, and they can solve

nonlinear optimizataion problems. The students are able to communicate their mathematical

kowledge in English.

Prerequisites:

Formal: -

Recommended: basic mathematical knowledge

Obligatory registration: yes Further Information on registration: Website of CDSB


Lecture / Block Seminar 2 10

ECTS in total 6

Form of assessment Written exam (120 min)


Lecturer/Person in charge Prof. Dr. Leif Döring



Language English

Program-specific educational goals

Grade graded

Range of application M.Sc. Econ., M.Sc. MMBR


Module: E 701 Advanced Microeconomics I

Contents: The course gives a foundation for studies for nicroeconomics at a PhD level. The first part is

devoted to consumer and producer theory. It is organized as follows:

• Consumer choice, the weak axiom of revealed preference and the law of demand (Mas-

Colell, Whinston and Green, Chapter 2)

• Classical demand theory, presenation theorem, utility maximization, expenditure

minimization and duality (MWG Ch. 3)

• Production, profit maximization and cost minimization (MWG Ch. 5)

• Choice under uncertainty, represenation theorem, money lotteries and risk aversion

(MWG Ch. 6)

The second part covers game theory and is organized as follows:

• Static games of complet information: Normal form games, existence of Nash equilibria

(Fudenberg and Tirole Ch. 1)

• Dynamic games of complete information: extensive form games, subgame perfection

and repeated games (FT, Ch. 3-5)

• Static games of incomplete informtaion: types, Bayesian equilibrium and purification

(FT, Ch. 6)

• Dynamic games of incomplete inforamtion: perfect Bayesian equilibrium, sequential

equilibrium and forward induction (FT, Ch. 8 and 11)

Learning outcomes: Students learn the baisc tools for graduate level microeconomic analysis. The concepts learned

in the course serve as building blocks for more advanced topics such as the ones studied in

Advanced Microeconomics 2 and 3 and also for macroeconomics and empirical studies.

Students also learn using rigorous formal proofs for microeconomic questions.

Prerequisites:

Formal: -

Recommended: E 700



Lecture 3 9

Exercise 1.5 2.5

ECTS in total 8

Form of assessment Written exam: 120 min (90% weighting)

Exercises (10% weighting)


Lecturer/Person in charge Prof. Dr. Nicolas Schutz



Language English


Grade graded


Range of application M.Sc. Econ., M.Sc. MMBR


Module: E 703 Advanced Econometrics I

Contents: The course is designed to offer an advanced treatment to econometric theory and applications.

Topics covered include: Repetition of ordinary least squares and generalized least squares,

instrumental variables estimation, simultaneous equations, generalized method of moments

and maximum likelihood estimation, time series and panel data econometrics. Attendance in

the lectures and exercise sessions are mandatory. Attempting exercise questions ahead of each

session and taking active part during the course of the sessions is essential.

Learning outcomes:

By the end of the module students will:

• have advanced theoretical knowledge in econometrics in the specific topics the module

covers.

• be familiar with current theories and recent developments in the specific topics of focus

for the module.

• have a higher/advanced level of analytical capability.

• have knowledge in the probabilistic background of advanced theoretical econometrics.

• be in a position to take on follow-up advanced theoretical and applied econometrics

modules.

• have a level of competence that permits independent undertakings in search of new

knowledge in the specialist areas the module covers.

• have a level of competence required to carry out (theoretical) research-oriented projects

independently.

• be in a position to exchange information, ideas, and solutions with experts of the field

on a scientific level as well as with laymen.

• be able to communicate and to work effectively and efficiently with people and in

groups.

• be able to communicate precisely in the English specialist language.

Prerequisites:

Formal: -

Recommended: E 700

Obligatory registration: yes Further Information on registration: Website of the CDSB


Lecture

Tutorial

2

2

6

6

ECTS in total 8

Form of assessment written exam (180 min., 90%), assignment (10 %)


Lecturer/Person in charge Prof. Dr. Johannes Voget



Language English



Grade graded

Range of application M.Sc. MMBR, M.Sc. Econ.


Module: E 829 Dynamic Models in Empirical Industrial Organization

Contents: Goals and Content of the Module: This course covers recent techniques in development and

estimation of dynamic models. Each topic will be related to one or more recent papers

(particular papers from the reading list will be assigned later). We will discuss in detail the

model, research question, sources of identification and estimation methodology in each

paper.

1. Single agent dynamics: Math preliminaries, Dynamic programming, Estimation methods

in dynamic models, Applications (bus engine replacement, patent renewal)

2. Dynamic Demand for differentiated products: Durable goods, Consumer inventory

behaviour

Learning outcomes: The students are expected to learn modern empirical methods in industrial organization with a

strong focus on dynamic decision making. In particular, they should be able to formulate a

model of optimal consumer and/ or producer decisions in an environment with state

dependence and estimate the structural parameters using appropriate data and numerical

techniques. Expertise gained in the course allows for using empirical methods to conduct

counterfactual simulations to quantitatively evaluate various governmental policies and

regulations. Successful completion of the course equips students with the knowledge necessary

to conduct an independent research project as a part of doctoral study.

Prerequisites:

Formal: -

Recommended: E 700- E703, E801- E805



Lecture 2 8

Exercise 1 3

ECTS in total 7

Form of assessment Completion of a computation problem set applying

several methods learned in the course


Lecturer/Person in charge Jun.-Prof. Kathleen Nosal, Ph.D.


Offering Spring semester

Language English


Grade graded

Range of application M.Sc. MMBR, doctoral programs


Module: E 880 Graduate Public Economics: Empirical and Quantitative Methods

Contents: The course covers empirical methods needed for research in Public Finance, with emphasis on

combining theoretical models with empirical evidence for the optimal design of tax and social

insurance policies. Topics include efficiency costs and incidence of taxation, income taxation,

behavioral responses to taxes and transfers, social insurance and capital income taxation.

Learning outcomes: Understanding of modern methods used in Public Finance and ability to implement them.

Understanding and knowledge of the research frontier in the field. Understanding how

empirical research and economic theory can be used together to address questions of optimal

policy design.

Prerequisites:

Formal: -

Recommended: E700 - E703, E801 - E806



Lecture 4 16

ECTS in total 10

Form of assessment Written Examination (50%),

Research Proposal (50%)


Lecturer/Person in charge Prof. Dr. Andreas Peichl, Jun.-Prof. Dr. Sebastian

Findeisen



Language English


Grade graded

Range of application M.Sc. MMBR, doctoral programs


Module: MKT 903 Advanced Business Econometrics

Contents: The goal of the course is to provide Ph.D. students an introduction in and overview of state-

of-the-art discrete choice methods in business and marketing research. Researchers use these

statistical methods to examine the choices that consumers, households, firms, and other agents

make. Each of the major models is covered: logit, generalized extreme value (including nested

and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on

these basics. Simulation-assisted estimation procedures are investigated and compared,

including maximum simulated likelihood, method of simulated moments, and method of

simulated scores. The course will also cover procedures for endogeneity and expectation-

maximization algorithms. Participants will study a variety of articles and case studies which

demonstrate the application of such models to real business phenomena.

Learning outcomes: Course participants will be able to aqcuire knowledge with respect to the following themes:

• Properties of Discrete Choice Model

• Logit Model

• Numerical Maximization

• Nested Logit

• Probit Model

• Mixed Logit

• Conditional Distributions of Individual-level Parameters

• Endogeneity: BLP, Control functions, Latent Instruments

By the end of the course, students will be able to apply these statistical methods to examine

the choices that consumers, households, firms, and other agents make. Each of the major

models is covered: logit, generalized extreme value (including nested and cross-nested logits),

probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-

assisted estimation procedures are investigated and compared, including maximum simulated

likelihood, method of simulated moments, and method of simulated scores. Students will

know procedures for drawing from densities, including variance reduction techniques such

as antithetics and Halton draws. Students will be able to ably these procedures in many fields,

including marketing, management or taxation but also energy, transportation, environmental

studies, health and labor.

Prerequisites:

Formal: -

Recommended: -



Lecture Seminar 2 10

ECTS in total 6

Form of assessment Written Exam (60 min.) 50 %, Home Assignments

50 %


Lecturer/Person in charge Prof. Dr. Florian Stahl


Duration of module 1 semesters (28 contact hours)


Language English


Grade graded

Range of application M.Sc. MMBR, Doctoral Program


Module: TAX 916 Applied Econometrics I

Contents: The course gives an applied introduction to the methodology employed in the empirical

research literature. The main topics include: Ordinary least squares, instrumental variables

estimation, and panel data econometrics. Further topics may also be included according to

demand by participants.

Learning outcomes: The covered material enables students to apply the econometric methods which are commonly

used in economic research. Special attention is given to the interpretation of empirical results

and understanding the potential caveats of different approaches.

Prerequisites:

Formal: -

Recommended: Basic knowledge of statistics.



Lecture 3 13

ECTS in total: 8

Form of assessment Oral exam (10 min.) 50 %, Class Participation 50 %


Lecturer/Person in charge Prof. Dr. Johannes Voget



Language English


Grade graded



Module: Cross-Sectional Data Analysis

Contents:

The main focus of this course lies on the introduction to statistical models and estimators

beyond linear regression useful to social scientists. We first repeat and deepen the basics of the

classical linear regression model (OLS). A good understanding of the classical linear

regression model is a prerequisite and required for the further topics of the course. We will

then discuss violations of the asymptotic properties of the linear regression model and ways to

address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The

second part of the class is dedicated to 1st) the maximum likelihood estimator and 2nd) to

limited dependent variable models for binary choice decisions (Logit, Probit), ordinal

dependent variables, and count data (Poisson, Negative Binomial).

Learning Outcomes:


(1) Know the different cross-sectional regression processes

(2) Be able to perform regression analyses using current statistic programs such as Stata or R

(3) have advanced knowledge of different estimation procedures (e.g. OLS, maximum

likelihood estimate)

(4) know regression diagnostics (5) have knowledge in model selection

Prerequisites:

Formal: -

Recommended: -

Obligatory Registration: Yes Further Information on registration:

“Student Portal”


Lecture 2 6 6

Exercise 2 6 3

ECTS in total: 6-9

Form of assessment Written exam (90 mins), oral exam, written term

paper


Lecturer/Person in charge Prof. Dr. Thomas Gautschi



Language English


Grade graded



Module: Multivariate Analysis

Contents:

The course introduces students to quantitative methods in political science. During the first

half of the course, we will focus on linear regression models. The topics covered include

discussions of the mathematical bases for such models, their estimation and interpretation,

model assumptions and techniques for addressing violations of those assumptions, and topics

related to model specification and functional forms. During the second half of the course,

students will be introduced to likelihood as a theory of inference, including models for binary

and count data.

Learning Outcomes:

The main goals of this course are to develop sound critical judgment about quantitative studies

of political problems, to understand the logic of statistical inference, to recognize and

understand the basics of the linear regression model, to develop the skills necessary to work

with datasets to perform basic quantitative analyses, and to provide a basis of knowledge for

more advanced statistical methods.

In the accompanying course "Tutorial Multivariate Analyses" students will develop the

necessary expertise in using statistical software to conduct quantitative research in political

science.

Prerequisites:

Formal: -

Recommended: -




Lecture 2 6 6

Tutorial 2 6 2

ECTS in total: 6-8

Form of assessment assignments include homeworks, mid-term exam,

data analysis projects





Language English


Grade graded



Module: Game Theory

Contents:

Game theory and other formal modelling techniques are powerful methodological tools that

are widely employed in political science and the social sciences, in general. The associated

mathematics and notation can, nevertheless, be bewildering and frustrating to the newcomer.

This course exposes students to the mechanics of a variety of formal models used in political

sciences, showing them the underlying logic of these models, as well as the surrounding

notation and mathematics. The overall aim of the course is to put students in a position where

they can more effectively read literature that employs game-theoretical modelling, and actually

make use of formal modelling techniques in their own work.

The tutorial accompanies the graduate-level introductory lecture in game theory. Its main

objective is to practice solution concepts for static and dynamic games of complete and

incomplete information. The contents are centered around the material covered in the lecture.

Thus, the following key areas will be discussed: preferences and individual choices, decision

theory, normal form games, Nash equilibria, extensive form games, subgame perfect

equilibria, repeated games, bargaining, games with incomplete and imperfect information,

Bayesian perfect equilibria, signalling games. At the substantial level, we will use these

concepts to study, for instance, candidate competition, political lobbying, and war and

deterrence. Students are required to submit weekly problem sets. Moreover, active

participation in class discussions is expected.

Learning Outcomes:

Ability in reading and writing down formal models in political science.

Prerequisites:

Formal: -

Recommended: -




Lecture 2 6 6

Exercise 2 6 2

ECTS in total: 8.0 6-8

Form of assessment Homework assignments (20%), mid-term exam

(30%), final exam (50%)


Lecturer/Person in charge Prof. Dr. Thomas Bräuninger

Anna-Sophie Kurella



Language English


Grade graded

Range of application Doctoral Program, M.Sc. MMBR


Module: Advanced Quantitative Methods

Contents:

This course serves as an introduction to a multitude of probability models that are appropriate

when the linear model is inadequate. After introducing the fundamentals from which statistical

models are developed, this course will focus on one specific theory of inference, namely on

the statistical theory of maximum likelihood. We will also devote considerable time to

statistical programming, simulating and conveying quantities of material interest of such

models (using R).

Learning Outcomes:

Knowledge of statistical theory of maximum likelihood, statistical programming, simulating

and conveying quantities of material interest of models, knowledge of R.

Prerequisites:

Formal: - Recommended: Knowledge of Multivariate Analysis




Lecture 2 6 6

Exercise 2 6 2

ECTS in total: 6-8

Form of assessment Students who wish to pass this course must complete

homework assignments and produce a research

paper. Participation in the tutorial session (2 ) is

necessary for the assignments which complement the

lecture (6 ).



Tilko Swalve



Language English


Grade graded



Module: Longitudinal Data Analysis

Contents:

Lecture "Longitudinal Data Analysis"

The lecture gives a broad overview of methods of longitudinal data analysis. The focus of the

course lies on methods for panel and event history data analysis and their use in the social

sciences. Attendance of the complementary course "Data Sources in Social Sciences" is highly

recommended as the course illustrates the practical application of the methods in Stata and

deepens understanding of the theoretical content of the lecture.

Lab Course "Data Sources in Social Sciences"

Using Stata we practice methods of event history and panel data analysis (especially first-

difference-models, random/fixed eff-models, event history analysis) with examples from the

German SOEP. Attendance of the complementary lecture " Longitudinal Data Analysis " is

highly recommended as firm knowledge of the lecture content is presumed. In addition, a

further prerequisite for participation is firm knowledge of data preparation and estimation of

simple linear regressions using Stata. Participation in the lab session in not mandatory for

meeting the overall course requirements. However, students should otherwise commit to

deepening and applying the contents of the lecture of their own.

Learning Outcomes:


1. be able to critically assess potential and problems of analyses based on cross-sectional

and longitudinal data

2. be able to carry out advanced data analyses, particularly event history and panel data

analyses

3. understand inferences using longitudinal data

4. know how to use statistic software to carry out analyses of longitudinal data

Prerequisites:

Formal: -

Recommended: -




Lecture 1 7 6

Exercise 1 7 3

ECTS in total: 6-9

Form of assessment 6 will be awarded for successful completion of an

exam and an additional 2 can be awarded for

participation in the lab course and handing of two

practical assignments.


Lecturer/Person in charge Prof. Dr. Tobias Wolbring



Language English



Grade graded



Module: Multilevel Modeling

Contents:

Multilevel modeling is used when observations on the individual level are nested in units of

one or more higher levels (e.g. students in classes in schools). The course will cover the logic

of multilevel modeling, its statistical background, and implementation with Stata. Applications

will come from international comparative research treating countries as the higher level units.

Data from the International Social Survey Program and the PIONEUR project (on intra-

European migration) serve as examples. However, students are also encouraged to bring their

own data.

Learning Outcomes:

Participants will be able to use multilevel modeling and write a research paper employing

this method.

Prerequisites:

Formal: Knowledge of regression analysis

Recommended: -


Website of the CDSS


Seminar 2

ECTS in total: 6

Form of assessment Assignments, presentation


Lecturer/Person in charge Prof. Dr. Michael Braun



Language English


Grade graded



Module: Modeling Social Processes

Contents:

This course provides an introduction to formal models in the social sciences. It discusses a

series of basic prototypes which have proved to be important tools for theory construction in

various fields. Topics covered are, for example, individual choice, exchange, strategic action,

collective action and the evolution of cooperation, diffusion, or segregation. While most of the

models and examples chosen might already be fairly well known, this course puts specific

emphasis on explaining the math behind them in more detail than usual.

Learning Outcomes:

Course participants will be able to acquire knowledge with respect to the following themes:

Individual choice, exchange, strategic action, collective action, and the evolution of

cooperation, diffusion or segregation.

By the end of the module students will have expertise in general formal skills, such as

maximizing under constraints, game theory, difference equations, differential equations,

Monte Carlo simulation, and agent-based simulation. Students are able in principle to modify,

extend or combine existing models according to their own research questions.

Prerequisites:

Formal: -

Recommended: -


Website of the CDSS


Seminar 2 10

ECTS in total: 6

Form of assessment Assignments, presentation


Lecturer/Person in charge CDSS Methods Professor



Language English


Grade graded



Module: Bayesian Statistics

Contents:

This course introduces and extends the classical “workhorse” social science models – linear,

logit, probit models and their multilevel extensions – from a Bayesian perspective.

The Bayesian approach to inference has attracted considerable attention in recent years. Mostly

this is due to the increasingly complex models that it allows to fit. However, one might easily

overlook the benefits that a Bayesian approach provides when estimating “standard”

generalized linear models.

The course will introduce the basics of Bayesian inference, showing how its interpretation of

probability differs from the classical approach and how it is actually closer to how social

scientists think about their models. The course then introduces generalized linear models and

shows how they can be easily fitted using modern software for Bayesian inference. It

introduces Bayesian model diagnostics and fit measures, which allow straightforward model

comparisons and examination of model misspecification.

The focus of the course will be on how to compute interesting quantities from those models,

like predicted values or first differences in expected values for a changing covariate. Using the

Bayesian approach to inference, their calculation is straightforward and one can easily

construct appealing graphical displays.

Learning Outcomes:

Understand and apply Bayesian Inference.

Prerequisites:

Formal: -

Recommended: You should be familiar with the basics of regression models and

maximum likelihood estimation. No previous knowledge of software for Bayesian inference

is required. However, we will use R as a front-end to JAGS and for graphical displays.

Resources to learn R basics are UCLA’s Stat Consulting Site as well as the official manuals at

http: //www.r-project.org/.


Website of the CDSS


Workshop 2 6

ECTS in total: 4

Form of assessment Up to 4 assignments


Lecturer/Person in charge CDSS Methods Professor



Language English


Grade graded



Module: Meta Analysis

Contents:

This course will assist students to prepare, conduct, and to write-up a systematic review and/or

meta-analysis for a M.Sc. or PhD thesis, encompassing the entire research synthesis process,

namely:

• Developing a problem statement and specifying research questions / hypotheses for a

systematic review / meta-analysis;

• Data collection (systematic retrieval and selection of studies);

• Data extraction, coding, and unifying effect sizes;

• Analysis and interpretation;

• Reporting / writing a thesis encompassing a systematic review / meta-analysis.

• Special emphasis will be on the analysis procedure (4) using R packages (esp. metafor:

www.metafor-project.org ).

Learning Outcomes:

Advanced skills in conducting and writing-up meta analyses.

Prerequisites:

Formal: -

Recommended:

• Contents of an introductory course on systematic reviews and meta-analysis (e.g., the

ones offered by the course instructor at the University of Mannheim in the following

semesters: FSS 2015, or HWS 2014, or FSS 2014).

• M.Sc. or PhD thesis topic has been (at least roughly) defined already

• Basic understanding of R ( http://www.r-project.org)


Website of the CDSS


Seminar 2 10

ECTS in total: 6

Form of assessment Research proposal, presentations


Lecturer/Person in charge Prof. Dr. Michael Bosnjak



Language English


Grade graded



B. Economics

Module: CC 502 Applied Econometrics

Contents:

In this module we will first review some essential statistical concepts, including probability,

random variables, distributions, correlation, sampling, estimation, confidence intervals and

hypothesis testing. The second part will provide a treatment of the principles and uses of simple

and multivariate linear regression analysis. We will consider how results from such analyses

can be interpreted and applied in the context of managerial decision-making. Furthermore, the

limitations and potential pitfalls of econometric techniques will be discussed.

Learning outcomes:

By the end of the module students will have:

• a sound understanding of key statistical concepts and techniques

• familiarity with the principles and core techniques of econometric analysis and how

regression results can be interpreted

• skills in the practical application of relevant quantitative methods

Prerequisites:

Formal: -

Recommended: Knowledge of introductory statistics at bachelor level



Lecture 2 6

Exercise 2 2

ECTS in total 6

Form of assessment Written exam (90 min.)


Lecturer/Person in charge Hidenori Takahashi, Ph.D.



Language English


Grade graded

Range of application M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. MMBR


Module: BE 510 Business Economics I

Contents:

In this module we will examine strategic behavior and efficiency in the context of business

decisions from a microeconomic perspective. An important aim is to convey an understanding

of how economists approach these issues and to introduce participants to some economic

concepts that may be helpful in making sense of how businesses and other organizations

operate. Using these concepts we will study the decision-making behavior of individuals,

analyze incentive mechanisms within organizations and investigate the economics of

bargaining and information.

Learning outcomes:


• the ability to apply economic reasoning in the context of economic organizations

• an understanding of model-based analytical methods and arguments in managerial and

personnel economics

• familiarity with relevant economic and game-theoretical concepts under both full and

incomplete information

Prerequisites:

Formal: -

Recommended: Knowledge of introductory microeconomics at bachelor level



Lecture 2 6

Exercise 2 2

ECTS in total 6



Lecturer/Person in charge Prof. Dr. Henrik Orzen

Duration of module 1 semester. Both the lecture and the Exercise will be

held in two parallel, identical streams to allow

smaller class sizes.


Language English


Grade graded



Module: BE 511 Business Economics II

Contents:

Building on BE 510 Business Economics I, this module will provide an introduction to

economic models of strategic decision-making and behavior of firms in the context of

oligopolistic competition. Topics will include output and pricing strategies, the economics of

collusion, market structure, market entry decisions and product differentiation. Some of these

topics require a degree of analytical rigor and we will make use of some game-theoretical and

mathematical methods.

Learning outcomes:


• the ability to apply economic reasoning in the context of oligopolistic competition

• an understanding of model-based analytical methods and arguments

• familiarity with basic game-theoretic models of industrial organization

Prerequisites:

Formal: -

Recommended: Knowledge of introductory microeconomics at bachelor level, BE 510



Lecture 2 6

Exercise 2 2

ECTS in total 6



Lecturer/Person in charge Prof. Dr. Henrik Orzen

Duration of module 1 semester. Both the lecture and the Exercise will be

held in two parallel, identical streams to allow

smaller class sizes.


Language English


Grade graded



C. Management

Module: CC 504 Corporate Social Responsibility

Contents:

The lecture deals with the topic “Corporate Social Responsibility” (CSR) and its related

backgrounds. The focus is on the discussion about the relation between profits and

responsibility. For this purpose, theoretical CSR foundations as well as practical CSR

competencies are imparted. It is presented that CSR is an investment in the conditions of long-

term business success. One of the central questions is how CSR can be managed under

competitive pressure. Managerial challenges in the field of CSR are illustrated by case studies.

Learning outcomes:

The lecture sensitizes participants for the relevance of ethical questions in daily business. The

imparted knowledge enables students to handle the prominent term “Corporate Social

Responsibility” appropriately. In particular, students are able to define the scope and the limits

of CSR with reasonable care. The imparted competencies allow students to structure and to

handle ethical challenges in management.

Prerequisites:

Formal: -

Recommended: -



Lecture 2 6

ECTS in total 4

Form of assessment Written examination (60 min.)


Lecturer/Person in charge Prof. Dr. Laura Marie Schons



Language English


Grade graded

Range of application M.Sc. MMM, M.Sc. MMBR, M.Sc. Bus. Edu., M.A.

K&W


Module: MAN 630 Introduction to Entrepreneurship

Contents: Bill Gates of Microsoft, Michael Dell of Dell Computers, Fred Smith of FedEx, and Dietmar

Hopp of SAP, are examples of successful entrepreneurs who have pursued new business

opportunities in a way which is innovative, path-breaking and contributing to growth and

employment generation. But, who is a typical entrepreneur, and how well does the typical start-

up perform? Why do people start businesses? What characteristics does the typical

entrepreneur have? How do entrepreneurs finance the typical start-up? What is the role of

networks for launching and running new businesses?

These are questions that will be answered in the course MAN 630 Introduction to

Entrepreneurship, which consists of two parts, namely Part I “Lectures” and Part II “Case

Studies.” This course combines both classical methods of teaching (lecturing) and interactive

methods of teaching (case studies). More exactly, theories presented in the first part will be

followed by demonstrating real-life cases in the second part. Additionally, guest speakers, i.e.

entrepreneurs and/or academics, will be invited to shed light on specific topics of interest.

The purpose of this course is to provide students with both theoretical knowledge of

entrepreneurship and practical skills for setting up businesses. Dealing with current and

classical theories and recent empirical evidence on entrepreneurship, Part I aims at giving

students a solid insight into entrepreneurship research. Part II gives the possibility to put

oneself in the shoes of an entrepreneur and to train entrepreneurial skills, thus giving a more

practical understanding on decisions on founding and operating start-ups.

Learning outcomes: By the end of this course, students will have gained fundamental perspectives in theoretical

and practical entrepreneurial mechanisms. They will have acquainted practice in case study

training.

Prerequisites:

Formal: -

Recommended: not specified

Obligatory registration: yes Further Information on registration: www.ifm.uni-mammheim.de / “Student Portal”


Lecture 2 10

ECTS in total 6

Form of assessment 100% Written final examination


Lecturer/Person in charge Prof. Dr. Michael Woywode



Language English


Grade graded


Range of application M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Bus. Inf., M.A.

K&W, M.Sc. Econ., LL.M., M.Sc. Econ., M.Sc.

MMBR


Module: MAN 632 Advanced Entrepreneurship

Contents: Successful entrepreneurs who have pursued new business opportunities demonstrate

managerial capabilities in which they lead their start-up to long-term profitability. How did

they manage the market entry and growth of their enterprises? What type of strategies did they

implement to achieve sustainable competitive advantage? These are the questions that will be

addressed in MAN 632 "Advanced Entrepreneurship."

Theoretical foundations of the course will lie in theories on managerial asp of entrepreneurship.

It is structured into two parts: First, “A Life Cycle View on High-Tech Entrepreneurship”

which will present a dynamic capabilities approach to entrepreneurial management.

Entrepreneurial managers need to have an in-depth understanding of the ongoing challenges

in key functional areas such as finance, innovation and technology management, human

resource management, firm succession, strategic management, management of

internationalization, and management of (fast) growth. Second, in the part “Industry Studies”

topics such as innovation in biotech industry, innovation in the software industry, and the role

of regional clusters will be addressed. Both researchers and guest presenters will give an insight

into entrepreneurial management.

Learning outcomes: By the end of this module, students will have gained fundamental theoretical and practical

knowledge in managing entrepreneurial ventures successfully.

Prerequisites:

Formal: -

Recommended: not specified

Obligatory registration: yes Further Information on registration: www.ifm.uni-mammheim.de / “Student Portal”


Lecture 2 10

ECTS in total 6

Form of assessment 100% written final examination





Language English


Grade graded

Range of application M.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Bus. Inf., M.A.

K&W, M.Sc. Econ., M.Sc. MMBR


Module: MAN 645 Leadership and Motivation

Contents:

This course is designed to introduce students to different perspectives on leadership and the

role that motivation plays in the leadership process. It provides students with a comprehensive

understanding of leadership theories (e.g., trait, skills, style, and situational approaches) and

discusses contemporary challenges of leadership in organizations (e.g., culture and leadership,

women in leadership positions, ethical leadership). Furthermore, the course gives students the

opportunity to experience their own leadership behavior and thereby help to prepare them for

leadership roles in organizations.

Learning outcomes:

By the end of the module students will be able to:

… understand different approaches to leading and motivating employees,

… analyze different leadership styles and evaluate their advantages and disadvantages,

… assess factors of successful leadership,

… understand potential problems in the interaction between supervisors and subordinates,

… learn about their own leadership style.

Prerequisites:

Formal: -

Recommended: Not specified

Obligatory registration: no Further Information on registration: -


Lecture 2 6

Exercise 2 2

ECTS in total 6

Form of assessment Written examination (60 min.), Assignment


Lecturer/Person in charge Prof. Dr. Torsten Biemann



Language English

Program-specific educational goals LG 2, LG 3

Grade graded


M.A. K&W, LL.M., M.Sc. Econ., M.Sc. MMBR


Module: MAN 647 Strategic and International Human Resource Management

Contents:

This course is intended to expand students’ knowledge on basic human resource management

(HRM) processes and instruments by adding an international and a strategic dimension. In the

first part of the module (international dimension), we examine the impact of national context

on HRM and discuss basic HRM functions in international organizations. Topics include

international recruiting and selection, training and development, compensation, and employee

relations.

In the second part of the module (strategic dimension), we focus on the dynamics of HRM

systems. Various approaches of how singular HRM practices conceptually and empirically

form bundles are considered. We review how HRM practices can be aligned with respect to

other HR practices (horizontal fit) as well as organizations’ objectives (vertical fit) to form

HRM systems that help improve organizations’ performance.

Learning outcomes:

By the end of the module, students will be able to:

… understand the impact of culture and national context on HRM,

… analyze differences in international HRM systems,

… design basic HRM practices in international organizations,

… align HRM practices with regards to their horizontal and vertical fit,

… comprehend the processes translating HRM systems into organization’s performance, and

… consider contextual factors’ impact on the alignment of HRM systems.

Prerequisites:

Formal: -

Recommended: -

Obligatory registration: yes Further Information on registration: Website of the chair / “Student Portal”


Lecture 2 6

Exercise 2 2

ECTS in total 6

Form of assessment Written examination (60 min.): 75%

Coursework: 25%


Lecturer/Person in charge Prof. Dr. Torsten Biemann / Dr. Jörg Korff



Language English


Grade graded


M.A. K&W, LL.M., M.Sc. Econ., M.Sc. MMBR


Module: MAN 648 Incentives and Performance

Contents:

In this course, we study performance management and analyze the link between incentives and

individual’s performance in organizations. Based on the performance management process,

the course covers performance measurement and monitoring, key performance management

tools, and implementation of performance management systems targeted at employees in

organizations. Furthermore, the course investigates the role of incentives in organizations, both

theoretically and empirically from a psychological and economic perspective.

Learning outcomes:

By the end of the module, students:

… will be able to select and implement appropriate performance measures,

… are familiar with formal performance appraisals,

… understand the link between pay and performance,

… are able to analyze the effectiveness of incentives both theoretically and empirically, and

… are able to design performance management systems.

Prerequisites:

Formal: -

Recommended: Module MAN 645 or MAN 647



Lecture 2 6

Exercise 2 2

ECTS in total 6

Form of assessment Written examination (60 min.): 75%

Coursework: 25%





Language English


Grade graded


M.Sc. Econ., M.Sc. MMBR


Module: Module: MAN 654 Corporate Restructuring

Contents:

This course exposes students to a broad range of strategic and financial restructuring

techniques (e.g., equity carveouts, spin-offs, leveraged buyouts) that can be applied to improve

business performance. We will engage in an analysis of the rationale underlying specific

corporate restructuring transactions, the process of executing these transactions, and their eff

on the welfare of managers, stockholders and other corporate stakeholders. Current business

cases and guest speakers representing the different parties involved in corporate restructuring

(e.g., investment bankers, consultants, business unit managers, employees) will help illustrate

how various corporate restructuring approaches may be applied in business practice to increase

firm value.

Learning outcomes:

Students will acquire and demonstrate expert knowledge in the specific subject area of

corporate restructuring. Students will be able to apply and critically reflect upon contemporary

theories, models and tools utilized in restructuring research and practice. When analyzing

business cases, students will be able to identify key issues, derive appropriate solutions, and

have a good understanding of their impact on restructuring success. Students will be able to

design and deliver professional and effective oral presentations of their business case analyses

Prerequisites:

Formal: -

Recommended: -



Lecture 2

8

Exercise 2

ECTS in total 6

Form of assessment Exam and presentations in Exercisees; attendance

required


Lecturer/Person in charge Prof. Dr. Matthias Brauer



Language English


Grade graded

Range of application M.Sc. MMM, M.Sc. Bus. Inf., Bus. Edu., LL.M.,

M.Sc. Econ., M.A. K&W, M.Sc. MMBR


Module: MAN 655 Corporate Strategy: Managing Business Groups

Contents: In this course, we will critically reflect upon the role of corporate centers of multinational

companies (MNCs). A particular emphasis will be placed on the challenges faced by corporate

centers and their opportunities for value creation in their quest to turn a conglomerate discount

into a conglomerate premium.

Learning outcomes:

Students will acquire and demonstrate expert knowledge in the specific subject area of

corporate strategy (i.e. about the challenges and value creation opportunities faced by corporate

centers of MNCs). Students will be able to apply and critically reflect upon contemporary

theories, models and tools utilized in strategy research and practice (i.e. the repertoire of

analytical tools applied by corporate centers to fulfill their “parenting” role). When analyzing

fictitious or real-life business cases, students will be able to identify key issues, derive

appropriate solutions, and have good understanding of their impact on strategic decision

outcomes. Students will be able to design and deliver professional and effective oral

presentations of their own strategy case analyses.

Prerequisites: Formal: -

Recommended: Knowledge in strategic management



Lecture 2

8

Exercise 2

ECTS in total 6

Form of assessment Exam and assignment/presentation; attendance

required





Language English


Grade graded

Range of application M.Sc. MMM, M.Sc. Bus. Inf., Bus. Edu., LL.M.,

M.Sc. Econ., M.A. K&W, M.Sc. MMBR


Module: MAN 801 Advances in Entrepreneurship and Management Research

Contents: This seminar will expose participants to the rich ecology of theoretical perspectives flourishing

in management research. Students are invited to develop creative research proposals

worthwhile to be developed into a strong dissertation based upon well grounded theoretical

perspectives.

Learning outcomes: The course aims at enabling students to understand basic concepts in management research

find appropriate theoretical concepts and lenses and apply them properly to their individual

research topics.

Prerequisites:

Formal: -

Recommended: -



Lecture 2 4

ECTS in total 6

Form of assessment Paper 80 %, Presentation 20 %





Language English


Grade graded



Module: MAN 802 Fundamentals of Non-Profit Management Science

Contents: This course addresses the relevant economic and managerial theories in order to be able to

analyze the economic behaviour and management of nonprofit organizations (NPOs).

Topics that will be discussed include “theories of nonprofit organizations”, “organizational

behaviour and performance”, “nonprofit management”, “financing nonprofit organizations”,

and “governance and accountability”.

Learning outcomes: This course aims to provide a basic understanding of the theory and management of nonprofit

organizations. Each student will be asked to read a basic scientific (“classical”) paper, enrich

this paper by adding latest research results from currently published journal papers, and present

the findings in class, where the results will be discussed.

Prerequisites:

Formal: -

Recommended: -



Lecture 1 12

ECTS in total 6

Form of assessment Presentation 60 %, Discussion 20%, Class

Participation 20 %


Lecturer/Person in charge Dr. Stijn Van Puyvelde



Language English


Grade graded



Module: MAN 804 Advances in Strategic Management

Contents: The seminar serves the purpose of familiarizing students with the most relevant research

streams and trends in strategy research. Besides a review of the current state-of-the-art, we will

engage in a discussion about the most prevalent theoretical lenses, key subject areas and

phenomena as well as the empirical designs applied by scholars in these areas.

Learning outcomes: By the end of the module students will:

• understand the most established as well as the latest emerging literature substreams in

strategy research

• have an overview of the most prevalently studied phenomena and subject areas in these

literature substreams

• be familiar with the theoretical and methodological approaches used to address the

different sets of research questions

• be able to capitalize on a critical reflection of the current state of the literature, to

develop a research proposal

Prerequisites:

Formal: -

Recommended: -



Seminar 2 6

ECTS in total 6






Language English

Program-specific educational goals LG 1, LG 3, LG 4

Grade graded



Module: MAN 805 Applied Methods in Management Research

Contents: This module offers an overview of the statistical procedures and methods that are relevant in

management research. After having gained a broad understanding of the methods that are

important in the respective literatures, students integrate this knowledge by examining some

exemplary research studies for each method and by asking how they would go about in

conducting their own research in this field. Students apply their knowledge from the seminar

presentations in several exercises. In particular, the course covers the following topics:

- Moderation and Mediation

- Control Variables

- Scales and scale analysis

- Common Method Variance

- Hypothesis testing

- Outliers

- Multicollinearity

- Missing data

- Multilevel modelling

Learning outcomes: By the end of the module students will be able to:

- Identify issues and problems in quantitative management research

- Perform statistical analyses in selected areas (e.g., multilevel modeling and scale

analysis)

- Design quantitative research proj that consider contemporary standards and

suggestions in management research

- Learn how to address methodological issues in research papers

Prerequisites:

Formal: -

Recommended: -



Seminar 2 10

ECTS in total 6

Form of assessment Oral exam (20 min.) 75 %, Presentation 25 %





Language English

Learning Goals LG 1, LG 2, LG 3

Grade graded



Module: MAN 806 Advances in Organization and Innovation Research

Contents: Students will gain an overview of fundamental topics in the fields of organization and

innovation.

The course starts with a kick-off. A list of required readings and a detailed course program will

be provided at this meeting. Then, students have one month to prepare their input for the

blocked seminar. During the blocked seminar, the papers, they will have read and prepared,

will be presented and discussed. Afterwards there will be a general discussion. Besides the

content itself, conceptual framing and methodology (strengths and weaknesses) will be

reviewed. The papers selected for presentation will cover different quantitative and qualitative

methods.

Learning outcomes: Students will learn to critically assess existing literature, to formulate research questions, to

frame theoretical contributions and to design and implement a research design to be able to

derive causal results.

Prerequisites:

Formal: -

Recommended: Master-level knowledge on organization and management; MAN 803 or

profound knowledge about econometric methods and their application in empirical

management research.



Seminar 2 10

ECTS in total 6

Form of assessment Presentation 50 %, Discussion 50 %


Lecturer/Person in charge Prof. Dr. Karin Hoisl



Language English


Grade graded



Module: MAN 910 Area Seminar Management

Contents:

The course focuses on current research topics in the field of management. Visiting researchers

present their latest working papers and discuss their ideas with participating faculty and

students. The course introduces students to the variety of research methods that are currently

popular in empirical and theoretical research.

Learning outcomes:

Students will learn to follow-up with and discuss about current research topics in management.

The interaction with leading researchers will allow them to develop own research ideas and

get insights into the design, execution and presentation of research proj.

Prerequisites:

Formal: -

Recommended: -



Seminar 2 -

ECTS in total 1

Form of assessment Class Participation


Lecturer/Person in charge Visiting Researchers


Offering Fall Semester and Spring Semester

Language English


Grade graded



D. Master’s Thesis

Module: MAN 950 Proposal Development

Contents:

In this course, students develop a research proposal for their Master’s thesis and give a

presentation on their work. Students first discuss potential topics of their Master’s thesis with

their supervisors and subsequently develop a research proposal that serves as a starting point

for their Master’s thesis.

Learning outcomes:

Students will train their scientific writing and presentation skills, and receive feedback for

the research topic of their Master’s thesis.

Prerequisites:

Formal: MAN 801, MAN 802, MAN 804, MAN 805, MAN 806

Recommended: -



Seminar 1 8

Consultation 1 8

ECTS in total 9





Offering Spring term

Language English

Learning Goals LG 1, LG 4

Range of application M.Sc. MMBR

Documents

Module Catalog “Mannheim Master in Business … of assessment Final exam (90 min., 100%) Preliminary course work - Duration of module 1 semester Offering Fall semester and Spring