DOCTORAL SUMMER SCHOOL EBS BUSINESS SCHOOL 2018 - EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb - Amazon AWS
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
DOCTORAL SUMMER SCHOOL
EBS BUSINESS SCHOOL
2018
Kursplanung 2018
Wahlkurse ST 2018
Max.
Dozent Kursname Umfang Kurstage Termine Beginn Ende
Teilnehmerzahl
SummerSchool 2018 (Spring Term)
Prof. Dr. Markus Kreutzer Theoretical Perspectives in Management Blockseminar 2,5 Tage, 24 TU /2ECTS 20 Montag 04.06.2018 09:00 17:00
Kursnummer: P.60-f Dienstag 05.06.2018 09:00 17:45
Mittwoch 06.06.2018 09:00 13:00
Prof. Dr. Sabine Benoit Survey Design and Measurement in Social Sciences Blockseminar 2,5 Tage, 24 TU /2ECTS 20 Mittwoch 06.06.2018 13:30 17:30
Kursnummer: P.55-f Donnerstag 07.06.2018 08:15 17:00
Freitag 08.06.2018 09:00 17:00
Prof. Dr. Karin Kreutzer Qualitative Research Methods for Doctoral StudentsBlockseminar 2,5 Tage, 24 TU /2ECTS 20 Mittwoch 13.06.2018 13:00 18:00
Kursnummer: P.51-f Donnerstag 14.06.2018 08:15 18:00
Freitag 15.06.2018 08:15 14:15
Prof. Jan Mutl, PhD Panel Data Econometrics Blockseminar 2,5 Tage, 24 TU /2ECTS 20 Montag 18.06.2018 09:00 17:45
Kursnummer: P.58-f Dienstag 19.06.2018 09:00 17:45
Mittwoch 20.06.2018 09:00 13:00
Dr. Stephan Ludwig Text Analysis Blockseminar 2,5 Tage, 24 TU /2ECTS 15 Mittwoch 20.06.2018 13:30 17:30
Kursnummer: 62-f Donnerstag 21.06.2018 08:15 17:00
EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Freitag 22.06.2018 09:00 17:00
Prof. Dr. Sven Heidenreich Fundamentals of Experimental Design and Analysis Blockseminar 2,5 Tage, 24 TU /2ECTS 20 Mittwoch 27.06.2018 13:00 17:00
Kursnummer: P.61-f Donnerstag 28.06.2018 08:15 17:00
Freitag 29.06.2018 08:15 16:15
Benjamin Elsner, PhD Introduction to Econometrics Blockseminar 2,5 Tage, 24 TU /2ECTS 20 Montag 02.07.2018 09:00 17:00
Kursnummer: P.57-f Dienstag 03.07.2018 08:15 17:00
Mittwoch 04.07.2018 08:15 12:15
Pflichtkurs ST 2018
Prof. Dr. Klaus Uhlenbruck Academic Writing & Publishing Blockseminar 1,5 Tage, 12 TU/2ECTS 20 Montag 11.06.2018 09:00 16:15
Kursnummer: P.25 Dienstag 12.06.2018 09:00 12:15EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Theoretical Perspectives in Management Course No. P. 60-f
Prof. Dr. Markus Kreutzer
Guest Speaker/-s: none
Contact: markus.kreutzer@ebs.edu
ECTS: 2
Number of Sessions: 12
Language: English
Shortbio:
Prof. Dr. Markus Kreutzer is Dean of EBS Business School since mid-June 2017 and
was Vice Dean Research of EBS BS from February 2016 until July 2017.
Markus Kreutzer holds a Master’s degree in Business Administration and a Master’s
degree of Economics from the University of Passau and a Doctoral Degree from the
University of St. Gallen. In his thesis at the intersection of strategy and corporate
entrepreneurship, he examined how multi-business firms effectively steer and
coordinate different types of strategic initiatives. Prior to joining EBS, he worked as
Assistant Professor of Strategic Management at the Institute of Management at the
University of St. Gallen in Switzerland and as Visiting Professor at the
Entrepreneurship Department of IESE Business School in Spain.
Markus Kreutzer has published his research in leading international (e.g., Academy of
Management Review, Strategic Management Journal, Long Range Planning) and
German journals (e.g., Harvard Business Manager). He is active in the scientific
community as reviewer for several leading international journals and conferences
(e.g., Academy of Management, Strategic Management Society) and is editorial board
member of the Journal of Management.
Markus Kreutzer teaches undergraduate, graduate, and PhD courses on strategic and
international management with a focus on inter-organizational strategy-making,
organizational growth, renewal, and adaptation, and business model innovation. He is
also actively involved in several Executive Education programs.EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Course Description: In this course doctoral students will be exposed to the rich ecology of theoretical
perspectives in management research. Based on a common vocabulary to facilitate
discussion about management theories, students are expected to understand the
boundary assumptions and constraints of the theories discussed, the concepts they
use and the proposed relations among those concepts. Students should be enabled to
select an appropriate theoretical perspective for their own research projects and
apply them properly. We will discuss a range of the most crucial theoretical
perspectives, including, for example, agency theory and transaction cost economics,
industrial organization economics, resource-, knowledge-, and capability based views,
as well as institutional and behavioral theory.
Required Readings: Bacharach, S.B. (1989): Organizational theories: Some criteria for evaluation,
Academy of Management Review, 14: 496-515.
For each theoretical perspective discussed there will be one required reading.
Further Recommended Jenkins, M., Ambrosini, V, Collier, N. (2016): Advanced Strategic Management – A
rd
Readings: Multi-Perspective Approach, Palgrave Macmillan, 3 edition
Assessment: 10% Participation: Students are expected to have read the required reading for each
theory and actively participate in the seminar`s discussion
30% Presentation of one theoretical perspective: Students are expected to present
one of the theoretical perspectives discussed based on the required reading
60% Research paper (individual assignment)EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Survey Design & Measurement in Social Science Course No. P.44-f
Prof. Dr. Sabine Benoit (née Moeller)
Guest Speaker/-s: None
Contact: s.benoit@surrey.ac.uk
ECTS: 2
Number of Sessions: 5
Language: English
Course Description: Measurement in natural science is often related to objective criteria (e.g.
weight, height and length). Social sciences in contrast often aim to measure
more abstract (latent) constructs like preferences, attitudes or perceptions.
The measurement of such latent constructs is thus not perfect in terms of
validity and reliability, but contains error. The aim of this course is to teach
students how to undertake studies that include measurement instruments
(e.g. an item-battery in a questionnaire) that have low systematic or random
error. The second aim of this course is to teach students how to evaluate
market research conducted by other authors.
Topics that will be covered by this course are the theoretical foundation of
conceptualization, operationalization and specification of constructs
(classical test theory, item response theory, indexing). Related topics within
the course are the set-up of a questionnaire, multi- versus single-item
measurement, the wording and order of questions as well as different types
of scales. Possible biases of a measurement instrument and methods to
decrease them are introduced and discussed (e.g. social desirability bias,
single-informant bias, common method bias, non-response bias). Beyond
that different methods of pre-testing measurement instruments are covered
(e.g. item sorting, index of homogeneity of placement). A further part of the
course covers the process of the data collection and within this this issue of
response rates and means to enhance them. The last part of the lecture will
cover the assessment of the measures (e.g. content, convergent or
discriminant validity).EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Required Readings: There will be no required readings beforehand. The course is designed as a
starting point for familiarizing with the topics.
Further Recommended Dillman, Don A. (2007), Mail and Internet Surveys: The Tailored Design
Readings: Method (2nd edition). New York: Wiley, Part One.
Pedagogy: The course will include interactive discussions and potentially some group
works. Students are welcome to bring their own measurement instruments
(e.g. questionnaires) to class to be discussed.
Assessment: Class participation (20%) and a take-home exam (80%).EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Qualitative Research Methods for Doctoral Students Course No.:P.51-f
Prof. Dr. Karin Kreutzer
Contact: karin.kreutzer@ebs.edu
ECTS: 2
Number of Sessions: 12 x 90 Min.
Course Prerequisites: None
Language: English
Course Description: This course aims at helping students to implement qualitative research methods
within their doctoral research and in constructing their dissertation. The course will
be particularly useful for PhD candidates in an early stage to gain an overview of
qualitative research methods; however you can also attend if you wish to further
elaborate the research design of your on-going qualitative study.
The course not only attempts to help PhD candidates in getting to know a ‘toolbox’
they can use for writing their dissertation, but also gives some general advice how
to avoid possible pitfalls within this process (e.g., with regard to an appropriate
project planning). We start by discussing and introducing three elements that are
constitutive for research in general (i.e. a research method, a theoretical
perspective, and a unit of analysis). Next, we discuss when to use qualitative
research methods and how to come up with appropriate samples. The main part of
the lecture is focused on data collection techniques. We discuss how to conduct
interviews (e.g., word questions in the right way) and observations, and how to take
field notes.
We also discuss how to analyze qualitative data (e.g., ‘grounded theory’) and how to
include it in a case study. Last but not least, we discuss some possible pitfalls that
are likely to occur when doing qualitative research in general and when writing
research papers that are aimed at being part of a Doctoral Thesis. The lecture is
supported by practical exercises and examples.
- Setting the Context – The Nature of Qualitative Research
- Designing Qualitative Studies
- ’Doing Fieldwork’ – Collecting Qualitative Data
- Analyzing Qualitative Data & Case Study Analysis
- Writing a Thesis – Some Possible Pitfalls
Presentation: Together with a colleague, you will present the qualitative
methodology of a high quality academic paper and critically comment upon it. The
respective articles will be send around by email approx. four weeks before the
course starts.EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Paper: Write a 5 - 8 pages paper explaining the qualitative methodology of your
dissertation in detail. The paper should be structured like the "methods chapter" in
your dissertation including information about your (potential) research design,
sample and sampling logic, the research setting (short description of the case(s));
and methods of data collection and data analysis you are applying or intending to
apply. Your paper should also contain a reflection on how you are planning to meet
the criteria for good qualitative research. Please cite the relevant literature for the
methods you chose.
Required Readings: Eisenhardt, K.M. (1989): Building Theories from Case Study Research, Academy of
Management Review, Vol. 14, No. 4, pp. 532-550.
Gephart, R. P. (2004): From the editors: Qualitative research and the Academy of
Management Journal, Academy of Management Journal, 47(4): 454-462.
Patton, M. Q.: Qualitative Research & Evaluation Methods, 3rd edition, Thousand
Oaks, 2002.
Yin, R. K.: Case Study Research, 2nd edition, Newbury Park, 2003.
Further Recommended Flick, U. (2006). An Introduction to Qualitative Research. London, Thousand Oaks,
Readings: New Delhi, SAGE.
Miles, M. B. and A. M. Huberman (1994). Qualitative data analysis. Thousand Oaks,
CA, Sage Publications.
Langley, A. & Abdallah, C. (2011): Templates and turns in qualitative studies of
strategy and management, Research Methodology in Strategy and Management, 6:
201-235.
Pedagogy: Lecture.
Assessment: Presentation, Paper (5-8 pages)
Workload Allocation: 90 h total student’s workload, thereof:
- Classes (12x90) 18h
- Pre-reading & Wrap up 18h
- Presentation 20h
- Paper (5-8 pages) 40hEBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Panel Data Econometrics Course No. P.58-f
Prof. Jan Mutl, PhD
Guest Speaker/-s: none
Contact: jan.mutl@ebs.edu
ECTS: 2
Number of Sessions: 12
Language: English
Course Description: This course will teach the students how to analyze and interpret empirical research
that employs panel datasets. We will extend our empirical toolbox for estimation
techniques such as generalized method of moments (GMM) and maximum likelihood
(ML) estimation. Topics covered will include static and dynamic panel data models
with fixed and/or random effects, and a brief introduction to spatial econometrics.
Prerequisites
Knowledge of basic linear regression techniques at the level of the Introduction to
Econometrics course
Required Readings: Main Text: Angrist and Pitschke: Mostly Harmless Econometrics
Supplement: Wooldridge: Panel Data Econometrics
Assessment: 100% ExamEBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Text Analysis Course No. 62-f
Dr. Stephan Ludwig
Guest Speaker/-s: none
Contact: s.ludwig@surrey.ac.uk
ECTS: 2
Number of Sessions: 12
Language: English
Introduction: It is increasingly noted by practitioners and theorists alike that text analysis has
emerged as a significant research methodology for any business discipline. Examples
are exponentially increasing as more and more information is becoming publically
available online and text-based communication (via email, SMS, messaging, blogs
and online user generated comments) is becoming the prime channel to exchange
information. Forrester Research shows that the market for text analytics has grown
to $978 million in 2014 from $499 million in 2011. Similarly the application of text
analysis in academic research is increasing across disciplines. Within the big data
phenomenon, Text analysis will take on a central role to inform business decision
making and facilitate academic research endeavors. Simply put, business research,
the methods that surround it, and the inferences derived from it have put business
as an academic discipline “on the map.” This has brought forward multiple
quantitative and qualitative methods such as preference data collection using
conjoint analysis, inferring market structure through multidimensional scaling,
inferring market segments through clustering routines or simply understanding the
underlying drivers of behaviors. Although these methods are here to stay, the
radical changes resulting from the heavy use of text-based communication promise
to fundamentally alter the data and collection methods used to perform these
methods.
Course Description: This series of seminars focuses on developing the necessary skills to conduct text
analysis, on any form of free, unsolicited, text-based information (e.g. open-end
survey responses, emails, social media posts). The seminars will cover what research
questions can be studied using text analytics, how to set up a text analysis study,
where and how to obtain data which is suitable and various text analytic approaches
and methods. We will further cover both bottom-up as well as top-down text
analysis techniques and using the software LIWC derive linguistic intensities of text-
based concepts. To round it all up the seminars will cover quantitative analysisEBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
techniques to empirically assess hypotheses using the results of text analysis and
explain how to write up the Method section for an A-level Publication.
Learning objectives: Students taking this summer course will be able to (1) develop an understanding of
a wide range of methods used to analyse text based data, (2) be able to select and
reason for appropriate methods for a variety of research questions, (3) acquire
hands-on experience using the text analysis software LIWC as well as quantitative
assessment methods, and (4) apply these skills directly to their own PhD projects.
Required Readings: Rather than following some textbook, this course will make use of the latest
publications in A-level journals across disciplines as well as reports by McKinsey,
Nielsen, Deloitte and Capgemini to provide students with a background
understanding of text analysis. For each Seminar there are readings suggested.
Software: LIWC (Available at: http://liwc.net/download.php)
Preparation for the To get the most out of this course you should attempt to work on a text based
dataset that you obtained for one or several of your PhD projects. The course is
course: designed so that you can bring your own text based dataset and conduct all
exercises directly using your own data. Note that this can be any text dataset,
spanning emails, online posts and messages, SMS, official statements, press
releases, company releases, survey responses in open text format etc. To ensure
you can also conduct quantitative analysis later on it is recommended to also have
some sort of outcome measure related to the text documents. So for example the
star rating of a customer review or its helpfulness rating on Amazon, a number of
views score for press releases, survey responses that are measured in Likert scale
format (e.g. satisfaction scores), corporate performance data etc. Should you have
no such dataset, or do not think that you can obtain something related through
web-scraping (which will be taught in this course) or other means then you will be
able to work on the datasets provided as course material instead.
Provisional Session June 20-22nd 2018
Outline: Text Analysis Date Topic
20.06.2018 Introductions & Building Blocks in Text Analysis
20.06.2018 Bottom-up Code-Based Approaches
21.06.2018 Top-down Deductive Approaches
21.06.2018 Applying Quantitative Analysis Techniques to Text Analysis
22.06.2018 Current Research Examples and supervised work on AssignmentEBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Assessment: Through the course you will develop your individual text analysis project. You will be
asked to conduct hands-on text analyses, prepare and submit a report on your final
results (100% of the final grade). This should be a brief report not exceeding 5 pages
(A4, line spacing: 1.5, Arial 11pt) including a brief introduction and managerial
relevance of your topic, the text analysis approaches you chose and used, the final
results and their implications for managers and academia. We will start the analysis
part of the work on the last day of the course. You may hand in the assignment at
any time but you must handin by 5th of July, 2018, before 5 p.m. at the latest by
email to s.ludwig@surrey.ac.uk.EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Fundamentals of Experimental Design and Analysis Course No. P.61-f
Prof. Dr. Sven Heidenreich
Guest Speaker/-s: none
Contact: Sven.heidenreich@ebs.edu
ECTS: 2
Number of Sessions: 12
Language: English
Course Description: This course is aimed at Ph.D. students who intent to conduct experimental and quasi
experimental research in business (e.g., marketing, organizational behavior) and
related disciplines (e.g., economics, psychology).
Experimental research is a method commonly used within business administration
especially for exploring consumer behaviour. Under experimental research a
collection of techniques is meant which use different manipulations to test causal
relationships. Usually one or more independent variables are manipulated to
determine their effect on a dependent variable.
The course will give an overview of the basics of experimental research. This includes
defining a research problem, transferring this problem into a research hypothesis and
developing a suitable experimental design and a suitable sample. The primary
objective of the course is to provide students with the concepts and tools needed for
collecting and analyzing experimental data. A secondary objective is to provide
students with the foundations for the methodological evaluation of other behavioral
researchers' work.
We will examine experimental designs and analyses from the perspective of an
applied behavioral researcher, not from that of a statistician. That is, we will
emphasize the actual use of proper data collection procedures and analysis
techniques for rigorous (i.e., publishable) theory testing. Although there will be
sufficient coverage of statistical concepts (to ensure that the procedures and
techniques are applied intelligently), we will not focus on statistical theory per se (as
would related courses in a statistics department).
In addition to the objectives mentioned above, the course will offer students an
opportunity to get started with the use of SPSS, one of the most widely used
statistical programming languages for manipulating and analyzing data. While this will
not be a course on SPSS itself, students should become comfortable with this
platform by the end of the course.EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Required Readings: Field, A.P. & Hole, G. (2003). How to design and report experiments. London: Sage.
Further Recommended Shadish, W.R., Cook, T.D. & Campbell, D.T. (2003) Experimental and Quasi-
Readings: Experimental Design for Generalized Causal Inference, Houghton-Mifflin.
Maxwell, S.E. & Delaney, H.D. (2004). Designing Experiments and Analyzing Data: A
Model Comparison Perspective (2nd ed). Lawrence Erlbaum: Mahwah, NJ.
Williams, L.J., Krishnan, A. & Abdi, H. (2009). Experimental Design and Analysis for
Psychology, Oxford University Press.
Seltman, H.D. (2012). Experimental Design and Analysis,
http://www.stat.cmu.edu/~hseltman/309/Book/Book.pdf
Keppel, G. & Thomas W. (2004). Design and Analysis: A Researcher’s Handbook, 4th
edition, Prentice Hall.
Calder, Bobby J. et al. (1981). Designing Research for Application, Journal of
Consumer Research, 8 (September), 197-207.
Lynch, John G., Jr. (1982). On the External Validity of Experiments in Consumer
Research, Journal of Consumer Research, 9 (December), 225-239.
Calder, Bobby J. et al. (1983). Beyond External Validity, Journal of Consumer
Research, 10 (June), 112-114.
Perdue, Barbara C. and John O. Summers (1986). Checking the Success of
Manipulations in Marketing Experiments, Journal of Marketing Research, 23
(November), 317-326.
Greenwald, Anthony G. (1976). Within Subjects Designs: To Use or Not to Use?
Psychological Bulletin, 83(2), 314-320.
Rosnow, Ralph L. and Robert Rosenthal (1989). Definition and Interpretation of
Interaction Effects, Psychological Bulletin, 105 (January), 143-146.EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Assessment: Students will have one main assignment:
Research Proposal: Students will design and propose an experiment either on a topic
of personal interest (study within their own dissertation) or on an assigned research
question. Apart from data collection, all steps of the research should be prepared,
presented, and discussed within a short research article. (written paper submission)
1. Introduction to the problem – why this should be studied
2. Short description of previous studies
3. Method
Variables and Manipulations
Data requirements
Process of data collection
Validity and reliability
4. Analysis
5. Anticipated results
Pedagogy: The course will include interactive lectures, in-class exercises using SPSS, and student
presentations (see above).
Assessment: Oral presentation and in class participation (40%), written paper submission (60%)EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Introduction to Econometrics Course No. P.57-f
Benjamin Elsner, PhD
Guest Speaker/-s: none
Contact: benjamin.elsner@ucd.ie
ECTS: 2
Number of Sessions: 12
Language: English
Course Description: This course will introduce the students to the basic tools required for empirical
research. Topics will include estimation and inference in linear regression models. We
will cover the classical linear regression model (univariate and multivariate models),
as well as instrumental variables. Time permitting, we will have a brief introduction to
limited dependent variable models (linear probability models, probit, logit), and to
causal inference with quasi-experimental methods (difference-in-difference,
regression discontinuity design).
The course will consist of lectures and computer labs. The lectures will introduce the
theoretical concepts behind the models mentioned above. During the computer labs,
students will apply the theoretical concepts to real data using Stata. A brief
introduction to Stata will be given.
Prerequisites
The course requires a basic understanding of statistical concepts. A brief statistics
review will be given in the first session.
Required Readings: Main Text: Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach.
Any edition is fine.
Further Recommended Econometrics Reference Manual: Greene, William H. Econometric Analysis. Prentice
th
Readings: Hall. Currently in its 7 edition. Previous editions are just fine.
Deeper coverage of the course material: Wooldridge, Jeffrey M. Economic Analysis of
nd
Cross-sectional and Panel Data. MIT Press, 2 edition.EBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb
Introduction to causal inference; Angrist, Joshua, and Steve Pischke. Mastering
st
‘Metrics. Princeton University Press, 1 edition.
The textbook on causal inference; Angrist, Joshua, and Steve Pischke. Mostly Harmless
st
Econometrics. Princeton University Press, 1 edition.
Assessment: Take-home examEBS Doctoral Summer School 2018vhjgvhjvhjkbbbnbnbnbnb Most courses have restricted capacity to ascertain quality and enable good discussions and class participation. Waiting lists will be created as there may be last minute drop outs and we may be able to fit you in at short notice. Written assessments, other than final course exams, are due 4 weeks after the end of the course, unless otherwise arranged with your lecturer. Please make time in your schedule to allow for the deadlines, especially if you have picked multiple courses. Final course exams are due for all students at the designated date set by the lecturer. Late submissions cannot be accepted and will be marked as fail. Credits are only awarded with a complete attendance record for each course. Attendance sheets will be provided for you to sign each day, and will be returned to the Office for Doctoral Studies via the lecturer at the end of the course. If you fail to attend a course without notifying your lecturer and the Office for Doctoral Studies, the course will be marked as a fail. If you are unable to attend due to sickness, please contact us immediately and be prepared to provide a doctor´s note. If you wish to change your participation in class to “observer” (observers will not be permitted to the examination) please inform the Office for Doctoral Studies IN ADVANCE of the course commencing, and change will be made manually. EBS Universität für Wirtschaft und Recht EBS Business School Rheingaustr. 1 65375 Oestrich-Winkel Contact Lisi de Jong Office for Doctoral Studies Phone +49 611 7102 2056 lisi.dejong@ebs.edu Tobias Wenderoth Office for Doctoral Studies Phone +49 611 7102 1572 tobias.wenderoth@ebs.edu
You can also read
