DOCTORAL SUMMER SCHOOL EBS BUSINESS … · international management with a focus on inter-organizational strategy -making ... management theories, ... Organizational theories: Some

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DOCTORAL SUMMER SCHOOL

EBS BUSINESS SCHOOL

2018


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Theoretical Perspectives in Management Course No. P. 60-f

Prof. Dr. Markus Kreutzer

Guest Speaker/-s: none

Contact: [email protected]

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.


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

Readings:

Jenkins, M., Ambrosini, V, Collier, N. (2016): Advanced Strategic Management – A

Multi-Perspective Approach, Palgrave Macmillan, 3rd

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)


Survey Design & Measurement in Social Science Course No. P.44-f

Prof. Dr. Sabine Benoit (née Moeller)

Guest Speaker/-s: None


ECTS: 2


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).


Required Readings: There will be no required readings beforehand. The course is designed as a

starting point for familiarizing with the topics.

Further Recommended

Readings:

Dillman, Don A. (2007), Mail and Internet Surveys: The Tailored Design

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%).


Qualitative Research Methods for Doctoral Students Course No.:P.51-f

Prof. Dr. Karin Kreutzer


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.


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

Readings:

Flick, U. (2006). An Introduction to Qualitative Research. London, Thousand Oaks,

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) 40h


Panel Data Econometrics Course No. P.58-f

Prof. Jan Mutl, PhD



ECTS: 2


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% Exam


Text Analysis Course No. 62-f

Dr. Stephan Ludwig



ECTS: 2


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 analysis


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

course:

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

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

Outline: Text Analysis

June 20-22nd 2018

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 Assignment

http://liwc.net/download.php


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 [email protected].


Fundamentals of Experimental Design and Analysis Course No. P.61-f

Prof. Dr. Sven Heidenreich



ECTS: 2


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.


Required Readings: Field, A.P. & Hole, G. (2003). How to design and report experiments. London: Sage.

Further Recommended

Readings:

Shadish, W.R., Cook, T.D. & Campbell, D.T. (2003) Experimental and Quasi-

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.

http://www.stat.cmu.edu/~hseltman/309/Book/Book.pdf


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%)


Introduction to Econometrics Course No. P.57-f

Benjamin Elsner, PhD



ECTS: 2


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

Readings:

Econometrics Reference Manual: Greene, William H. Econometric Analysis. Prentice

Hall. Currently in its 7th

edition. Previous editions are just fine.

Deeper coverage of the course material: Wooldridge, Jeffrey M. Economic Analysis of

Cross-sectional and Panel Data. MIT Press, 2nd

edition.

mailto:[email protected]


Introduction to causal inference; Angrist, Joshua, and Steve Pischke. Mastering

‘Metrics. Princeton University Press, 1st

edition.

The textbook on causal inference; Angrist, Joshua, and Steve Pischke. Mostly Harmless

Econometrics. Princeton University Press, 1st

edition.

Assessment: Take-home exam


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 [email protected] Tobias Wenderoth Office for Doctoral Studies Phone +49 611 7102 1572 [email protected]



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DOCTORAL SUMMER SCHOOL EBS BUSINESS … · international management with a focus on inter-organizational strategy -making ... management theories, ... Organizational theories: Some