BE INSPIRED

"The CDSB core courses and especially the electives are very useful because they equip us with solid skills in our field of research as well as in related fields." Kirstin Becker, CDSB

Course Catalog

Spring 2019


Course Type: core course

Course Number: ACC 802

Credits: 6

Prerequisites

Recommended: Basics in agency-theory, decision theory and game theory


Course Content

This course considers the use of accounting information in firms. In particular, the focus is on the use of accounting information for decision making and stewardship. From a methodology perspective game-theoretic models covering the following fields are considered:

1.             Accounting

2.             Auditing

3.             Corporate Governance

Learning outcomes: Students are able to understand and discuss game-theoretic representations of accounting issues. Further, they are able to set up simple models on their own.

Form of assessment: Essay 50 %, presentation 50 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
14.05.19
Tuesday
10:15
17:45
B6, 30-32, room 230
15.05.19
Wednesday
08:30
17:45
B6, 30-32, room 230
22.05.19
Wednesday
08:30
17:45
B6, 30-32, room 211
23.05.19
Thursday
08:30
17:45
B6, 30-32, room 211

Lecturer(s)


Course Type: core course

Course Number: ACC 903

Credits: 6

Course Content

This course provides a comprehensive overview of research topics and core methods in influential seminal as well as contemporaneous papers in the empirical accounting literature. In particular, we cover after an (1) introduction and a review of some “Accounting Classics”, the literatures on (2) Earnings Management, (3) Valuation (value relevance, earnings response coefficients (ERC)/event studies, accounting-based valuation), (4) Voluntary Disclosure, (5) Mandatory Disclosure, (6) International/-Institutional Accounting and IFRS, (7) Corporate Narratives, and (8) Bank Accounting. In each session, there is first an overview lecture introducing core methods in the corresponding field, and second a session in which we will jointly discuss in more depth selected empirical methods. Students are expected to prepare all readings and to lead selected class discussions about assigned papers in the second half of each lecture.

The lectures and student discussions are supplemented by exercise sessions in which we discuss broader related topics such as which fields are currently ‘en vogue’ in the journals, how to ‘stay informed’ and identify potentially relevant regulatory changes, how to know about topics influential researchers are currently working on, or discuss where students see their individual strength and how they can become competitive researchers in the future.

Learning outcomes: Students should know about the core issues of existing accounting research and established empirical research methodologies. They should also be able to place current research into the literature and to critically evaluate its relevance and technical rigor, and therefore to develop meaningful research ideas to extend current knowledge.

Form of assessment: Exam (90 minutes) 50 %, paper presentations and exercise sessions 50 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
19.02.19
05.03.19
Tuesday
10:00
13:00
O 254
26.03.19
09.04.19
Tuesday
10:00
13:00
O 254
30.04.19
Tuesday
10:00
13:00
O 254

Lecturer(s)


Course Type: core course

Course Number: ACC 904

Credits: 6

Course Content

The course provides PhD students with an introduction to current topics and methods in empirical accounting research.  The course aims to survey a wide variety of empirical research in in this field.  The course covers methodological issues, theoretical background, and selected empirical papers.  The assigned papers serve as examples to illustrate challenges of empirical research in accounting.

The course is structured around different identification approaches that are frequently used in recent accounting research.  This structure reflects the increasing importance of empirical strategies to address causality concerns in empirical accounting papers.  Since research in econ and finance took a lead on these questions, a number of examples from these fields will be integrated into the course.

Learning outcomes: Students are able to understand and evaluate research questions, contribution, and research methods of current research papers on accounting-related issues. Students are also able to develop new research questions based on their knowledge of the accounting literature.

Form of assessment: Exam (90 minutes) 50 %, presentation 50 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
01.04.19
24.06.19
Monday
10:00
13:00
O 254


Course Type: core course

Course Number: ACC 910

Course Content

The course focuses on current research topics in the field of accounting and taxation. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. For each presentation, a separate preparation session for the Ph.D. students is offered in advance by rotating faculty. Overall, the course deepens the students’ insights into a 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 accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 

 



Course Type: core course

Course Number: ACC 920/TAX 920

Course Content

The course is taught in a seminar-style format. Students present their own research ideas at different stages of the project (early ideas, preliminary results, and complete working papers). The presentations involve an interactive discussion between faculty and students about the project’s potential contribution, related literature, research design and interpretation of results.

Learning outcomes: Students will learn how to present and discuss their own research results in a scientific format. They will become acquainted with acting as a discussant for other topics. Students will gain insights into the assessment of contribution, research design, and interpretation of research papers. The development of these skills is also helpful for writing scientific referee reports.

Form of assessment: Class Participation


Coursedates will be announced via email to registered participants.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
13:45
17:00
SO 133

Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

Lecturer(s)


Course Type: core course

Course Number: FIN 620

Credits: 6

Course Content

This course is for participants of FIN 901 only.

There is abundant evidence suggesting that the standard economic paradigm of rational investors does not adequately describe behavior in financial markets. Behavioral Finance examines how individuals' attitudes and behavior affect their financial decisions. This course reviews recent research on possible mispricing in financial markets due to the nature of psychological biases. Moreover the course deals with behavioral finance models explaining investor behavior or market anomalies when rational models provide no sufficient explanations. Topics will include among others overconfidence, prospect theory, heuristic driven biases and frame dependence.

Learning outcomes: Behavioral finance applies scientific research on human and social cognitive and emotionalbiases. After completing this course, students will be able to better understand economic decisions and how they affect market prices and returns. They will know how behavioral findings are integrated with neo-classical theory.

Form of assessment: Written exam (60 min.)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
14.02.19
11.04.19
Thursday
08:30
11:45
O 131
Tutorial
20.02.19
29.05.19
Wednesday
12:00
13:30
O 133

Lecturer(s)


Course Type: core course

Course Number: FIN 802

Credits: 8

Prerequisites

FIN 801 Discrete-Time Finance


Course Content

Itô calculus, stochastic differential equations, Black-Scholes theory, hedging and arbitrage pricing of European, American, and exotic options, complete and incomplete market models, consumption investment problems, term structure theory for volatility and interest rates, default risk

Learning outcomes: The course aims at providing the basic concepts and techniques for modelling and analysing financial price processes in continuous time.

Form of assessment: Term paper 90 %, participation 10 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.02.19
Friday
09:45
18:00
B6, 30-32, room 211
01.03.19
Friday
10:00
18:00
B6, 30-32, room 211
08.04.19
Monday
10:00
15:45
B6, 30-32, room 211

Lecturer(s)


Course Type: core course

Course Number: FIN 803

Credits: 6

Prerequisites

Formal: E 701, E 703, FIN 801

Recommended:

  • A first-year doctoral level course in microeconomics that covers game theory and information economics (signaling, adverse selection, equilibrium refinements)
  • A first-year doctoral level course in econometrics that covers estimation and testing theory.
  • Some familiarity with corporate finance and financial institutions at the level of a masters level course is also assumed, but not essential. If you have no prior knowledge of corporate finance, then some chapters in an MBA-level textbook (e.g. Brealey, Myers, and Allen, Principles of Corporate Finance, 11th edition, McGraw Hill 2013; Berk and DeMarzo, Corporate Finance, 3rd edition, Pearson 2013) would be useful.

Course Content

This course is intended to enable students to understand and conduct research in corporate finance. It is taught at a first-year doctoral level.

Learning outcomes: The course combines two objectives. Firstly, participants learn the classic contributions to the theory of modern corporate finance and understand the main contributions to the field. Secondly, the course also introduces some of the main empirical contributions to the field and studies the main econometric and statistical techniques used in corporate finance. At the end of the course participants should be familiar with the main empirical and theoretical tools used in corporate finance.

Form of assessment: Essay (take-home exam)

For actual course details please click here


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
08.03.19
Friday
08:30
12:30
L9, 1-2, room 409
29.03.19
Friday
08:30
12:30
L9, 1-2, room 409
05.04.19
Friday
08:30
12:30
L9, 1-2, room 409
03.05.19
Friday
08:30
12:30
L9, 1-2, room 409
17.05.19
Friday
08:30
12:30
L9, 1-2, room 409
31.05.19
Friday
08:30
12:30
L9, 1-2, room 409

Lecturer(s)


Course Type: core course

Course Number: FIN 804

Credits: 6

Prerequisites

We assume basic knowledge in mathematics and statistics at the Bachelor level.

Recommended: Students should have successfully completed the 2-semester finance module of the Mannheim Bachelor program (or equivalent). It is recommended but not required that the students have participated in the Mathematics for Economists (E 700) and the Discrete Time Finance (FIN 801) courses.


Course Content

The aim of this course is to provide students with econometric skills to conduct independent research analysis in the broad field of empirical finance. The course is comprised out of three main components: (i) lectures, (ii) programming sessions in Matlab; and (iii) seminar. The lecture material focuses on several commonly applied estimation and testing techniques in empirical asset pricing in time-series and cross-sectional dimensions. We cover concepts such as the GRS test, the Fama-MacBeth approach, conditional and unconditional models and tests, factor-mimicking portfolios, finite-sample bias, and long-horizon predictability tests, amongst others. In a seminar component, students conduct a small research project and prepare a term paper on one of the topics covered in class.

Learning outcomes: The course provides students with a knowledge of several prevalent econometric concepts in finance and contributes to students’ ability to plan and carry out independent research in the broad field of empirical finance.

Form of assessment: Written exam (90 minutes) 40 %, class participation 60 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.03.19
20.03.19
Wednesday
14:00
18:00
11.03.19
18.03.19
Monday
09:30
13:30
25.03.19
Monday
08:00
15:00

Lecturer(s)


Course Type: core course

Course Number: FIN 901

Credits: 2

Course Content

FIN 901 is a continuative course of FIN 620. In this course students discuss and present current research topics in behavioral finance.

Learning outcomes: Students learn to critically discuss current research papers, i.e. data, methodology, and reasoning.

Form of assessment: Presentation


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-Off
14.02.19
Thursday
11:45
12:00
O 131
19.02.19
09.04.19
Tuesday
13:45
15:15
L9, 1-2, room 409

Lecturer(s)


Course Type: core course

Course Number: FIN 910

Course Content

The course focuses on current research topics in the field of finance. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. 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 finance. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 


Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

Lecturer(s)


Course Type: core course

Course Number: IS 807

Credits: 9

Course Content

This course provides an overview of qualitative research methods and their application in the field of Information Systems (IS). The course begins with an introduction to the basic principles and alternatives of conducting qualitative research. It then provides deeper insights into three types of qualitative research, i.e. positivist variance-theoretic, interpretive (and/or grounded theory based), and process theoretic. For each of them, the underlying principles will be discussed with illustrative examples. The students are required to summarize and discuss particular research papers and to reflect on how the principles of conducting qualitative research were applied in the respective papers. For particular topics the students will be grouped into teams and will be required to prepare their group work and present it in class. Overall, the course is designed to be interactive. For a final paper, the students illustrate the application of particular methods to design their own qualitative research study.

Form of assessment:  Term paper 50%, presentation 30 %, discussion 20 %

The course will take place during April and May. Exact dates tba.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
07.05.19
Tuesday
10:00
13:30
L 15, 1-6, room 422/423
08.05.19
Wednesday
10:00
13:30
L 15, 1-6, room 422/423
04.06.19
Tuesday
10:00
13:30
L 15, 1-6, room 422/423
05.06.19
Wednesday
10:00
13:30
L 15, 1-6, room 422/423

Lecturer(s)


Course Type: core course

Course Number: IS 903

Credits: 8

Course Content

Knowledge creation and dissemination are key objectives of scientific endeavors. However, what constitutes knowledge is a highly contested issue. Certainly, at the core of social science disciplines, knowledge is inseparable from theory. Indeed, to seek theory-guided explanations of real-world phenomenon is what separates scholars from consultants, who seek to change reality without explaining it, and from journalists, who report reality but do not explain it. The pursuit of theory drives us to understand reality—to discover truth—before making recommendations on how to change reality. To pursue theory is to pursue knowledge; to pursue knowledge is to advance humanity. Consequently, many scholars emphasize the centrality of theories for any scientific endeavor—a thought widely reflected in many disciplines from the natural to the social sciences. While attention to theoretical work has been at the heart of the Information Systems (IS) discipline for a long time, the focus on theoretical debates and genuine conceptual contributions has been picking up recently. This is reflected by a number of journal sections and conference tracks dedicated to advancing theory and theorizing in IS research just as much as in many authors’ experiences during the reviews of their work.

This course invites participants to join the ongoing discourse on theories and theorizing in the IS research community. It is designed to help participants build and extend their understanding of the nature and role of theory in IS research. Through discussions and analyses of current theoretical developments in the IS discipline and some of its main reference disciplines, participants will engage with theory and advance their skills of crafting their own theoretical contributions and evaluating those of others.

Learning outcomes:

  • Understand the importance and usefulness of theory in research
  • Learn theorizing strategies
  • Learn to evaluate theoretical contribution in research
  • Develop basic theorizing skills
  • Identify a theory that could be applicable to the participants’ own research programs

Form of assessment: Term paper 60%, presentation 20%, discussion 20%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
24.06.19
Monday
14:00
18:00
L 15, 1-6, room 714
25.06.19
Tuesday
14:00
18:00
L 15, 1-6, room 714
26.06.19
Wednesday
14:00
18:00
L 15, 1-6, room 714
27.06.19
Thursday
14:00
18:00
L 15, 1-6, room 714
28.06.19
Friday
14:00
18:00
L 15, 1-6, room 714

Lecturer(s)


Course Type: core course

Course Number: IS 910

Course Content

The course focuses on current research topics in the field of information systems and operations 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 information systems and operations 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 projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 


Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254

Lecturer(s)


Course Type: elective course

Course Number: IS 911

Credits: 8

Prerequisites

Fundamentals in computer science and programming


Course Content

This course covers principles and foundations of context-aware computing. Approaches to context acquisition, reasoning and management are presented and current trends in research are discussed.

Learning outcomes: Students will gain foundational knowledge about context-aware computing. They will learn about modelling and using context in computing systems. This includes context management and reasoning. After this module, students will know about the current state of the art in context-aware computing.

Form of assessment: Assignment 40 %, Discussion 40 %, Class Participation 20 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
19.02.19
28.05.19
Tuesday
13:45
15:15
L 15, 1-6, room 714/715

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

Lecturer(s)


Course Type: core course

Course Number: MAN 801

Credits: 6

Course Content

This seminar will expose participants to the rich ecology of theoretical perspectives flourishing in management and entrepreneurship research. Students are invited to develop and present 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 and entrepreneurship research, identify appropriate theoretical concepts and lenses and apply them properly to their individual research topics.

Form of assessment: Essay 80 %, presentation 20 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
19.02.19
Tuesday
16:00
18:00
L9, 1-2, room 210


Course Type: core course

Course Number: MAN 804

Credits: 6

Course Content

The seminar serves the purpose of familiarizing doctoral students with the most relevant research streams and trends in strategy research. We will read and discuss current state-of-the-art research with a special focus on the recent scholarly debate in the “Strategic Management Journal”, we will reflect the most prevalent theoretical lenses, key subject areas and phenomena as well as the empirical designs applied by scholars in this particular domain. Moreover, we will discuss the art of article writing for dedicated field journals.

Learning outcomes:

  • Develop an understanding of the most established as well as the latest emerging literature substreams in strategy research.
  • Gain an overview of the most prevalently studied phenomena and subject areas in these literature substreams.
  • Become familiar with the theoretical and methodological approaches used to address the different sets of research questions.

Form of assessment: Essay 60 %, presentation 40 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.03.19
Friday
16:00
18:00
L9, 1-2, room 210
29.03.19
Friday
10:00
18:00
L9, 1-2, room 210
12.04.19
Friday
10:00
18:00
L9, 1-2, room 210
13.04.19
Saturday
10:00
18:00
L9, 1-2, room 210


Course Type: core course

Course Number: MAN 807

Credits: 6

Course Content

This course provides an introduction to the fundamental methodological issues that arise in experimental and quasi-experimental research. Illustrative examples are drawn from the behavioral sciences with a focus on the behavior of consumers and employees. Topics that are covered include: the development of research ideas; data collection and reliable measurement procedures; threats to validity; control procedures and experimental designs; and data analysis. Emphasis is placed on attaining a working knowledge of the use of regression and analysis of variance methods for experimental data.

Participants are encouraged to develop their own ideas for experimental studies that will be presented, discussed, and developed further in class. Participants will collect and analyze own data and write a short research paper over the course of the semester.

Learning outcomes:

Through participating the in course, participants will:

Train their scientific writing and presentation skills, and receive feedback concerning their research ideas Gain an understanding of methodological issues that arise in experimental and quasi-experimental research Attain the skills needed to plan and execute their own experiments and analyze the resulting data

Form of assessment:  Term Paper


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
14.02.19
Thursday
10:15
13:30
O 048
28.02.19
Thursday
10:15
13:30
O 048
07.03.19
Thursday
10:15
13:30
O 048
28.03.19
Thursday
10:15
13:30
O 048
09.05.19
Thursday
10:15
13:30
O 048
23.05.19
Thursday
10:15
13:30
O 048


Course Type: core course

Course Number: MAN 910

Course Content

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 projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 


Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254


Course Type: elective course

Course Number: MAN 808

Credits: 6

Course Content

This course provides introductions into organization theories such as Max Weber’s Theory of Bureaucracy, Taylorism, Behavioral Theory, Theories of Organizational Evolution, Neoinstitutional Organization Theory, Network Theory, Interpretative Organizational Theories Theories, or Luhmann’s Organization Theory.

Learning outcomes: Students are able to evaluate the relevance of organization theories for the explanation of organizational phenomena. They are also able to apply organization theories in formulating research questions. They develop an understanding for the specific capabilities of organization theories.

Form of assessment: Presentation of a theory from the perspective of an interested newcomer (without grading); Paper which discusses the application of an organization theory to a self-chosen organizational problem 75%; Class Participation 25%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.02.19
Tuesday
09:00
13:00
L9, 1-2, room 001
08.03.19
Friday
09:00
20:00
O131
10.04.19
Wednesday
09:00
20:00
L9, 1-2, room 409
11.04.19
Thursday
09:00
18:00
L9, 1-2, room 001

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

Lecturer(s)


Course Type: core course

Course Number: MKT 804

Credits: 6

Course Content

This course teaches students how to develop and test theories in an applied and concrete way. We discuss and study a range of research approaches and methods, including structural equation modeling and experiments. This course provides students with an opportunity to develop and to fine-tune appropriate and specific theories for their own research.

Learning outcomes: Students come up and choose a specific topic of their interest in the beginning of the class and develop and present a theoretical framework suitable for their project. The latter part of the course is geared towards designing means to test the proposed theory. Another key learning outcome is to enhance students’ ability to conduct sound academic research and help them to derive hypotheses for their own research projects.

Form of assessment: Project (40%), presentation (60%)



Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
20.03.19
Wednesday
13:45
15:15
L5, 1 – room 009
03.04.19
Wednesday
13:45
17:00
L5, 1 – room 009
10.04.19
Wednesday
13:45
17:00
L5, 1 – room 009
08.05.19
Wednesday
13:45
17:00
L5, 1 – room 009
22.05.19
Wednesday
13:45
17:00
L5, 1 – room 009

Lecturer(s)


Course Type: core course

Course Number: MKT 901

Credits: 6

Course Content

In this course, students will develop their own marketing research projects (e.g., as parts of their own dissertation projects). In presentation sessions, students will present their research project to all participants of the class and to the instructor. Discussions among partici­pants as well as the instructor’s feedback aim at strengthening and refining the positioning and the contribution of the individual projects. Students in the first year of their Ph.D. studies can thus use this course to get important insights for the preparation and refinement of their disser­ta­tion proposal.

At the beginning of the course, objectives, general guidelines, and best practices for developing impactful research projects will be provided in a kick-off meeting. Furthermore, best practices how to get published in leading journals will be discussed. Then, students will start developing their projects. Students are not limited with respect to the choice of their individual research topic; however, it is made in accordance with the instructor.

Students will prepare the project by developing a presentation containing the positioning and research questions, a brief literature review, the theoretical foundations and research hypotheses, as well as an outlook on potential methodological approaches (such as obtaining and analyzing adequate data). Students will present their research projects. Based on the course participants’ and the instructor’s feedback, students can update and refine their research projects.

Learning outcomes:

  • Development of own marketing research project
  • presentation of own marketing research project
  • providing feedback on marketing research projects

This course aims at preparing students to formulate their own marketing research problems (e.g., as parts of their dissertation projects), to shape their contribution with respect to the existing literature, and to identify the necessary data and methods to conduct their research projects. As benchmark for the students’ research projects, the actual standards with respect to innovative­ness, relevance, and rigor of the leading international marketing journals will be applied. Furthermore, implications for practice have to be considered.

Form of assessment: Essay (60%), presentation (40%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.03.19
Wednesday
09:00
12:00
L5, 1 – room 009
30.04.19
Tuesday
09:00
12:00
L5, 1 – room 009
02.05.19
Thursday
09:00
12:00
L5, 1 – room 009
07.05.19
Tuesday
09:00
12:00
L5, 1 – room 009

Lecturer(s)


Course Type: core course

Course Number: MKT 910

Course Content

The course focuses on current research topics in the field of marketing. 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 marketing. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 


Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254

Lecturer(s)


Course Type: elective course

Course Number: MKT 803

Credits: 8

Course Content

This course takes an 'information processing' perspective to examine consumer behavior. The key focus will be to examine how consumers process marketing stimuli and make decisions. This includes topics such as motivation, attention and comprehension, memory, attitudes and attitude change, and decision making models. Both classic and current papers on these topics will be discussed.

The goal of this seminar is to provide insights into research and content issues in Consumer Behavior. Students will read key research papers on important topics and critically evaluate the studies.

Learning outcomes: Students will gain insights into research and content issues in Consumer Behavior. Students will read key research papers on important topics and critically evaluate the studies. Students will be expected to read assigned articles prior to class and be prepared to discuss them.

Course evaluation: Essay (50 %), presentation (25 %), participation (25 %)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
06.05.19
Monday
09:00
12:00
L5, 1 - room 009
08.05.19
Wednesday
09:00
12:00
L5, 1 - room 009
15.05.19
Wednesday
09:00
12:00
L5, 1 - room 009
17.05.19
Friday
09:00
12:00
L5, 1 - room 009

Lecturer(s)


Course Type: elective course

Course Number: MKT 902

Credits: 6

Course Content

The primary goal of Advances in Marketing Research is to help students prepare to conduct research which is publishable in the leading research journals in their respective disciplines.  Hence, the feedback students receive will be consistent with that dispensed by the reviewers and editors of the most prestigious research journals in business (i.e., highly critical).  Even when a manuscript is accepted for publication at a leading journal, the authors typically receive mostly negative comments on their work.  It is important that students not take criticism of their research personally.  To do so would be extremely ego deflating and would interfere with their subsequent performance on other assignments.  Moreover, students need to develop the ability to accept and use criticism to be able to survive in the academic publishing world.

Learning outcomes: Advances in Marketing Research is designed to assist doctoral candidates in acquiring a deeper understanding of the research process and a knowledge of the research tools which they will need to design and execute scientific research on behavioral and organizational issues in marketing.  An effort is made to help the students develop research judgment as well as research skills so that they will be better able to assess when a proposed piece of research is likely to be fruitful and when it is not.

Form of assessment: Essay: 50%, presentation: 30%, discussion and simulation/statistical analysis: 20%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.02.19
31.05.19
Friday
15:30
17:00
L9, 1-2, room 009

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas


Course Type: core course

Course Number: OPM 802

Credits: 8

Prerequisites

Recommended: Fundamentals in mathematics and statistics


Course Content

The course introduces some fundamental techniques for stochastic modelling and optimization, and it discusses their application in supply chain research. Key topics include:

  • Stochastic processes
  • Markov chains
  • Stochastic dynamic programming
  • Inventory theory
  • Revenue management

The course is taught in a seminar-style format.

Learning outcomes: The course aims to introduce the participants to fundamental stochastic modeling techniques. Upon completion of this course, participants should be able (i) to read and understand corresponding academic papers and (ii) to develop and analyze stochastic models for supply chain management issues.

Form of assessment: Term paper 30%, presentation 70%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.02.19
28.05.19
Tuesday
15:30
17:00
SO 318

Lecturer(s)


Course Type: core course

Course Number: OPM 806

Credits: 8

Prerequisites

Recommended: Fundamentals of statistics


Course Content

A large part of research in operations management focusses on modeling and solving practical problems. In contrast to this “OR approach”, the objective of empirical research is to collect data about practical phenomena in order to describe, explain, or predict how those phenomena work. This module provides an overview of (mainly quantitative) empirical research approaches to investigate research questions in operations management and related fields. The focus in not on the comprehensive treatment of empirical research methods, but on how to proceed from having a basic research question to an appropriate research design and methodology. Hence, special emphasis will be placed on the importance of understanding the contingent relationship between the nature of the research question and the research design used to answer it. Topics covered include quantitative vs. qualitative empirical research, framing of research questions, engaging theory and grounding of hypotheses, measurement and operationalization, sampling, model specification, and mainstream research designs and methodologies. This will enable students to critically evaluate the quality of the majority of empirical research in operations management and to design convincing research of their own.

The course will be taught using an interactive seminar style and is based on the discussion of a selection of papers.

Learning outcomes: At the end of this course, students have gained the competence to initiate, design, implement, and evaluate empirical research in the social sciences as applied to operations management.

Form of assessment: Oral exam (30 minutes) 60%, presentation 40%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
15.02.19
31.05.19
Friday
08:30
13:30

Lecturer(s)


Course Type: core course

Course Number: OPM 910

Course Content

The course focuses on current research topics in the field of information systems and operations 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 information systems and operations 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 projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 


Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254

Lecturer(s)


Course Type: elective course

Course Number: OPM 918

Credits: 8

Course Content

This elective course aims at PhD students in Operations. The course is taught in a seminar-style format. Each student gives three presentations about one own research project based on a draft of a paper. The aim is to discuss and sharpen the contributions of that work. The presentations are structured similar to papers in that field:

1.     Models: Problem description, Model formulations, and contributions to scientific literature

2.     Methods: Analytical or algorithmic approaches

3.     Managerial Insights: Structured properties, data analysis, and numerical results

Students act as discussants to presentations of other students. At the end of the seminar students hand in a draft of the paper, which reflects the discussions to each single point.

Learning outcomes: Students will learn how to structure and discuss their own research results for a presentation and for a paper. They will become acquainted with acting as discussant for other topics. They will learn how to identify and sharpen the contributions of their own work. They learn how to present the analysis of data and how to design numerical studies.

Form of assessment: Presentations during the course (60%), active contribution to class discussion (15%), draft of paper (25%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.02.19
28.05.19
Tuesday
08:30
11:45
SO 115


Course Type: elective course

Course Number: OPM 999

Credits: 9

Course Content

This course revolves around a research-oriented project that is carried out by the participants on an individual basis. Supported by their supervisors, participants will gain further experience in conducting research and will practice for the preparation of research proposals and own working papers. Topic and research question(s), project structure, and methodology are chosen independently by each student.

Learning outcomes: The main intended learning outcome is to learn and apply the competences for conducting high-quality research studies in operations management. In addition, participants will practice their skills in how to present research findings.

Form of assessment: Essay (70%), presentation (30%)


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-Off
11.02.19
Monday
13:45
15:15
SO 318

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas


Course Type: core course

Course Number: ACC 920/TAX 920

Course Content

The course is taught in a seminar-style format. Students present their own research ideas at different stages of the project (early ideas, preliminary results, and complete working papers). The presentations involve an interactive discussion between faculty and students about the project’s potential contribution, related literature, research design and interpretation of results.

Learning outcomes: Students will learn how to present and discuss their own research results in a scientific format. They will become acquainted with acting as a discussant for other topics. Students will gain insights into the assessment of contribution, research design, and interpretation of research papers. The development of these skills is also helpful for writing scientific referee reports.

Form of assessment: Class Participation


Coursedates will be announced via email to registered participants.


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
13:45
17:00
SO 133

Lecturer(s)


Course Type: core course

Course Number: TAX 801

Credits: 8

Course Content

This course integrates tax law with national and international tax planning. The main topics include:

  1. Fundamentals of tax planning and tax neutrality
  2. The choice of the organizational Form
  3. Flat tax and dual income Tax
  4. International tax planning
  5. Effective tax rates

Learning outcomes: The course gives guidance to students who are interested in the impact of taxes on the decisions of firms. The focus is on investment and financing decisions as well as on location decisions both from a national and from an international perspective.

Form of assessment: Presentation 50%, class participation 50%


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
13.02.19
29.05.19
Wednesday
08:30
11:45
SO 133

Lecturer(s)


Course Type: core course

Course Number: TAX 910

Course Content

The course focuses on current research topics in the field of accounting and taxation. Visiting researchers present their latest working papers and discuss their ideas with participating faculty and students. The presentations have workshop format and are similar in style to leading scientific conferences. For each presentation, a separate preparation session for the Ph.D. students is offered in advance by rotating faculty. Overall, the course deepens the students’ insights into a 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 accounting and taxation. The interaction with leading researchers will allow them to develop own research ideas and get insights into the design, execution and presentation of research projects.

Form of assessment: Oral participation.


Seminar Dates are announced here.


 



Lecturer(s)


Course Type: elective course

Credits: 6

Prerequisites



Course Content

In this course, students will learn how textual analysis methods work and how they can be implemented in Python.

In the first part, students will discuss prominent papers on textual analysis. The papers will cover the most commonly used methods for textual analysis, e.g. the bag-of-words approach and basic machine learning methods like Naïve Bayes.

The second part introduces frequently used text databases. For instance, the EDGAR (Electronic Data Gathering, Analysis, and Retrieval System) of the Security and Exchange Commission and LexisNexis will be covered in detail.

The third and largest part of the course deals with the implementation of textual analysis methods using the programming language Python. After a brief introduction to Python’s programming basics, students will use Python to automatically retrieve data from text databases (e.g. EDGAR) and the internet. In the second step, students will learn how to edit texts and how to identify and extract specific information from documents. Next, they will learn how to program dictionary-based textual analyses. Subsequently, they will analyze further characteristics of texts like language complexity and document similarity. In the last section, students will apply machine learning methods.

As part three starts with a general introduction to Python, it is not required to have any previous knowledge or experience with Python.

As the methods covered in this course can be applied to many different settings, the course targets students from all tracks (e.g. economics, finance, marketing, and management).

Students should install Phyton on their laptop before the course. An installation manual will be provided.

Learning outcomes:

  • Students will learn to implement state-of-the art research methods and approaches for analyzing verbal information in the fields of accounting, finance, and economics.
  • Students will learn how to incorporate research methods from computer linguistics to expand the current state of knowledge and arrive at new findings in economics and finance.
  • Students will obtain a solid programming knowledge in Python.

Form of assessment: Assignment


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
18.02.19
Monday
10:00
17:00
B6, 30-32, room 211
20.02.19
Wednesday
10:00
17:00
B6, 30-32, room 211
22.02.19
Friday
10:00
17:00
B6, 30-32, room 211


Course Type: elective course

Course Number: COLLOQUIUM

Credits: 1

Course Content

Identify strategies for reducing CO2 emissions. Examine technological developments, business models and public policy.

The colloquium is open to doctoral students at the University of Mannheim, the ZEW, post docs, and researchers in the field of sustainable energy and decarbonization. Target Audience: researchers in economics and business.

Learning outcomes: Identify the state-of-the art in current work on carbon reduction strategies.

Form of assessment: class participation


Registration: Instructor’s permission to enroll. Please contact Prof. Reichelstein: reichelstein(at)uni-mannheim.de


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
Kick-off
05.03.19
Tuesday
18:00
19:00
O 254

Lecturer(s)


Course Type: elective course

Course Number: RES

Credits: 5

Prerequisites

This is a Restricted Course for students who are currently doctoral students at the GESS of the University of Mannheim. It is intended for beginning as well as advanced doctoral students. This course is an elective course and counts as a 'Bridge Course'.


Course Content

This course aims at fostering the interdisciplinary spirit of the graduate students at the GESS. Participants will attend and participate at the GESS Research Day and the Science Speed Dating event in order to discover their potential for interdisciplinary and collaborative work. Participation at the GESS Research Day will include presenting an on-going working paper, discuss a presentation from another field of study and write a referee report about it, actively participate in discussions with students from different centers with matching research interests and participate in one discussion panel. The idea of the discussion panels is to bring together students from different centers to discuss core topics of societal relevance. Within these panels, the students should talk about how their own field might contribute to the discussion of a specific topic and ideally come up with some joint interdisciplinary research ideas.

During the Science Speed Dating event, course participants will discuss with graduate students from other departments and develop at least one collaborative research proposal. The proposal will be presented in a third meeting around one month after the speed dating.

 

 Assessment:

  • Presentation, discussion (including a three-page referee report), and participation in discussion panel at GESS Research Day. An extended abstract and the set of slides that will be used for the presentation or (preferably) a working paper draft needs to be provided by each presenting student to the assigned discussant two weeks before the research day.
  • Three pages individual reflection of the Research Day. Exemplary questions you can discuss in this document include (a) what you learned for your own project based during the day, (b) what new/unexpected topics you discovered, and (c) where you see potential collaborations or new research ideas. You can include answers to one or some of these or other questions in your reflection.
  • Participation at Science Speed Dating event.
  • Five pages interdisciplinary research proposal (group of two students) and presentation of this proposal
  • Detailed rules and schedules will follow.
  • Only pass/fail

Please register by latest February 15th,2019, by sending a title and an abstract of the research project/topic you would like to present to registration@gess.uni-mannheim.de. Please indicate in your e-mail your fields of interest and if you have any, mention up to three broad other fields (e.g. Marketing, Macroeconomics, Social Psychology) you would like to collaborate with.

Please note that the course is limited to a maximum of 24 participants, and seats will be allocated on a first come first served basis (conditional on fulfilling the course prerequisites).

Course dates:

-          Research Day: March 26th, 2019 (whole day symposium)

-          Speed Dating: May 7th, 2019

-          Presentation of research proposal: tbd, around one month after Speed Dating event


Competences acquired

  • Present own research in front of a general audience
  • Discuss work from another field
  • Develop and present own interdisciplinary research ideas

Lecturer(s)


Course Type: elective course

Course Number: TAX 913

Credits: 10

Course Content

This course gives an introduction to the main subjects and methodologies of empirical taxation research. Important landmark papers as well as contributions from the current research frontier will be discussed. If the relevant data is available, students get the chance to understand the empirical approach in practice in the computer lab. Following topics may be included:

  • Tax incidence
  • Tax efficiency
  • Taxes in the context of mergers and acquisition
  • Taxation of multinationals
  • Tax avoidance and tax evasion
  • Capital taxation
  • Labor taxation

 

Learning outcomes: The course should enable participants to identify gaps in the existing literature and to evaluate the potential of new research ideas. As a primary objective, the course supports students in developing their empirical research projects.

Form of assessment: Essay and/or presentation


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.02.19
28.05.19
Tuesday
15:30
17:00
SO 133
14.02.19
23.05.19
Thursday
10:15
11:45
O 226/28
15.02.19
31.05.19
Friday
15:30
17:00
SO 133

Lecturer(s)


Course Type: elective course

Course Number: TAX 917

Credits: 8

Prerequisites

Recommended: TAX 916 Applied Econometrics I or E703 Advanced Econometrics I


Course Content

The course covers applied empirical methods continuing where the Applied Econometrics I module leaves off. Potential topics can be determined according to demand and may include limited dependent variable regressions (binary, multinomial, sample selection, count data), matching estimators, quantile regressions, bootstrapping and programming. The emphasis on particular topics can be adjusted according to demand.

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.

Form of assessment: Presentation 50 %, Class Participation 50 %


Schedule

Type
From
To
Weekday
From
To
Room
Material
Lecture
12.02.19
28.05.19
Tuesday
08:30
10:00
O 226/28

Register

Business Spring 2019

ACC 802
Analytical Research in Accounting
ACC 903
Empirical Accounting Research I: (Research Methods)
ACC 904
Empirical Accounting Research II
ACC 910
Area Seminar Accounting
ACC 920/TAX 920
Brown Bag Seminar
Textual Analysis
COLLOQUIUM
Pathways to Decarbonization
RES
Interdisciplinary Work in Economics and Social Sciences (Bridge Course)
FIN 620
Behavioral Finance
FIN 802
Continuous Time Finance
FIN 803
Corporate Finance
FIN 804
Econometrics of Financial Markets
FIN 901
Behavioral Finance
FIN 910
Area Seminar Finance
IS 807
Designing Qualitative Research Projects
IS 903
Information Systems Theories
IS 910
Area Seminar Information Systems
IS 911
Context-Aware Computing
MAN 801
Advances in Entrepreneurship and Management Research
MAN 804
Advances in Strategic Management
MAN 807
Experimental Research in Management
MAN 910
Area Seminar Management
MAN 808
Organization Theories
MKT 804
Theory Development and Model Building
MKT 901
Designing Marketing Research Projects
MKT 910
Area Seminar Marketing
MKT 803
Consumer Behavior
MKT 902
Advances in Marketing Research
OPM 802
Dynamic and Stochastic Models in Supply Chain Research
OPM 806
Empirical Research in Operations Management
OPM 910
Area Seminar Operations Management
OPM 918
Business Analytics: Models, Methods, Managerial Insights
OPM 999
Project Study Operations
TAX 801
Business Taxation
TAX 910
Area Seminar Taxation
TAX 913
Empirical Taxation Research
TAX 917
Applied Econometrics II