Category Archives: THESIS

VHB ProDok Kurse

Philosophy of Science –

Foundations and Implications for Research Designs and Research Methods

Die wissenschaftstheoretische Analyse ist eine Grundlage für die Beurteilung eigener und fremder Forschungsleistungen. Während jedoch bei der Lektüre eines wissenschaftlichen Aufsatzes oder Monographie die eingesetzten Methoden, wie z.B. ein Fragebogen zur Erhebung von Daten oder eine Regressionsanalyse zur Auswertung von Daten, zu dokumentieren sind, wird die zugrunde liegende wissenschaftstheoretische Position häufig nicht einmal erwähnt. Wer sich jedoch mit Begründungsansprüchen wissenschaftlicher Forschung und ihrer Methoden auseinandersetzen will, kommt um eine Beschäftigung mit eben diesen Positionen nicht herum. Insbesondere bei mixed-method-Studien werden zudem solche Expositionen auch von Gutachtern und Journal-Editoren zunehmend eingefordert.

Ziel des Kurses ist es daher, die Teilnehmer und Teilnehmerinnen mit den wesentlichen wissenschaftstheoretischen Strömungen und ihren für die Wissenschaft wesentlichen ontologischen und epistemologischen Positionen vertraut zu machen. Der Einstieg ins Thema erfolgt auf der Basis dreier Leitfragen im Rahmen eines „World Cafés“, die den Verlauf des Kurses strukturieren. Die Grundlagen der Wissenschaftstheorie werden von einem Dozenten vorgestellt und dienen einer gemeinsamen Diskussionsbasis für die verschiedenen Positionen. Anschließend werden die verschiedenen Strömungen der Wissenschaftstheorie und ihre wesentlichen Implikationen für die Forschungsmethoden und das Forschungsdesign durch die Teilnehmenden präsentiert, wobei jeweils Themenschwerpunkte gebildet werden. Die individuellen Präsentationen dienen dann als Grundlage für die anschließende gemeinsame Diskussion und ggf. thematische Erweiterung durch die Dozenten.

Eine wesentliche Zielsetzung des Kurses ist es, für die verschiedenen Strömungen der Wissenschaftstheorie zu sensibilisieren und grundlegende Kenntnisse über verschiedene wissenschaftstheoretische Begründungsstrategien zu vermitteln, um eine kritische Reflektion über die eigene wissenschaftliche Forschung zu ermöglichen. Obwohl der Schwerpunkt des Kurses auf der Diskussion philosophischer (wissenschaftstheoretischer) Positionen liegt, ist es ein weiteres Anliegen des Kurses, deren Bedeutung für die wissenschaftliche Praxis zu thematisieren und anhand praktischer Beispiele zu analysieren.

 

Für diesen Kurs ist eine 18- bis 20-seitige schriftliche Ausarbeitung, eine 30-minütige Präsentation sowie leitende Fragen für die anschließende Diskussion vorzubereiten, die von den Vortragenden moderiert wird.

Zeitnah nach der Anmeldefrist werden die Themen verteilt.

 

Datum:

05. – 08. November 2024

Ort:

Harnack-Haus Tagungsstätte der Max-Planck-Gesellschaft
Ihnestr. 16-20
14195 Berlin

 

Kurssprache:

Deutsch

Referenten:

Prof. Dr. Rolf Brühl,

Lehrstuhl für Unternehmensethik und Controlling, ESCP Business School Berlin,
rbruehl@escp.eu

Prof. Dr. Thomas Wrona,
Institut für Strategisches & Internationales Management, Technische Universität Hamburg,
thomas.wrona@tuhh.de

Anmeldefrist:

Klicken Sie hier für Informationen zu Gebühren, Zahlung und Anmeldung,
oder schreiben Sie uns eine E-Mail: prodok@vhbonline.org.,

 

Anmeldefrist: 31. Juli 2024

VHB ProDok Kurse

Advanced Topics and Experimental Methods in Consumer Research

This course provides an overview of research methods and theories in consumer behavior. The topics covered in this seminar should be of interest to doctoral students studying Business, Psychology, Organization Behavior, and Marketing.

The course has two major goals:

  1. To expose students to research in specific areas of consumer research and marketing and to familiarize them with findings in these areas. These broad areas are emotions, sustainable consumer behavior, and human-computer interactions.
  2. To equip students with abilities to conceptualize, design and implement original consumer behavior research, particularly experimental research.

Students will read a set of core readings in order to gain knowledge of relevant theoretical foundations, methodological norms, and most recent findings. We will discuss papers published at top journals in consumer behavior, marketing, management, and psychology. One of the best ways for doctoral candidates to understand a research area is to critically review articles describing research in that area. The course will challenge students to adopt a critical stance when reading papers. This approach provides a deeper understanding of specific issues, a better appreciation of the research process, and training in research skills. During the discussion of the key articles on each topic, students will be challenged to review these articles. The class discussion will evaluate the articles and the reviews.

Students will learn to identify gaps in the literature or to apply a research problem to unexplored related phenomena.

Moreover, the second aim of this course is that students develop their own actionable research question and methodological plan. For this, they will be able to collect first data in the lab (BreLab) for their idea(s) (if physical course is possible), with the group and recruited students serving as participants in the respective experiments. If time-wise not possible, an online data collection will be done. Students will present their idea and potential results and give one another feedback on these ideas.

After completing this course, students will be able to define research questions in various domains of consumer behavior and in different empirical settings, they will know how to implement their research questions into an actionable experimental design, and they will be aware of potential problems and (dis)advantages in their research design.

Date:

25. – 27. September 2024 (Präsenz)

7. Oktober 2024 (Online)

 

Location:

Universität Bremen
WiWi2 F3290
Enrique-Schmidt-Str. 1
28359 Bremen

Language:

English

Lecturer:

Prof. Dr. Kristina Klein

University of Bremen

Registration:

Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.

Registration Deadline: 25. August 2024

 

VHB ProDok Kurse

Endogeneity in Applied Empirical Research

Many empirical research projects in business and economics that use non-experimental data struggle with the proper identification of causal effects of independent variables (e.g., price, management decisions) on dependent variables (e.g., demand, firm performance). The reason is that the identification of a causal effect hinges on the untestable assumption that the error term of a model is uncorrelated with the independent variables. If this assumption is not met, a model is plagued by endogeneity.

The topic of endogeneity has received considerable attention, and it is probably the most frequently encountered troublemaker in a review process at an academic journal.

This course therefore has the goal of making students familiar with the problem of endogeneity and potential remedies. This implies that it will cover the opportunities and problems associated with traditional approaches (e.g., Instrumental Variable estimation, Matching, Difference-in-Difference) as well as more recent developments (e.g., Gaussian Copulas; Machine Learning and Causal Inference; Synthetic Control Methods; Directed Acyclical Graphs (DAG)). The course will also cover how the data structure (e.g., panel data) can be utilized to address the problem.

Because the literature on endogeneity is often quite technical, this course aims at providing an easily accessible approach to this topic. Special emphasis will also be given to understanding when endogeneity indeed poses a real problem as compared to settings in which endogeneity is less likely to be a real threat to the validity of the findings.

After completing this course, students will be able to define and describe endogeneity problems in different empirical settings, they will have a better understanding of whether and when causal identification is possible how to implement techniques that address endogeneity, and they will be aware of the (dis)advantages of different methods.

Date:

18.9.2024 online and 25.-27.9.2024 face-to-face

Location:

Online & University of Tübingen

Language:

Englisch

Lecturer:

Prof. Dr. Dominik Papies
Universität Tübingen

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Participation fee:

Non-member: 650,00 Euro
VHB-member: 530,00 Euro

Registration deadline: August 18, 2024

 

Accomodation

The University of Tübingen Guest House has a limited number of rooms available that can be booked here: https://uni-tuebingen.de/en/12231.

 

VHB ProDok Kurse

Quantitative Empirical Accounting Research and Open Science Methods

This course focuses on quantitative empirical accounting research, covering theoretical, methodological and technical aspects of this research program. It also introduces students to the concepts of Open Science. In terms of applications, it concentrates on financial and non-financial reporting issues but also touches on some managerial and auditing topics. After this course, participants should

  • have a clear understanding about the theoretical foundations of quantitative empirical accounting research,
  • know the methodological approaches to and common pitfalls of empirical research designs,
  • have become familiar with a collaborative open science workflow using R/Python/Stata and Github,
  • know how to execute empirical archival studies, including the usability and inter-operability of different data sources
  • and, based on their own research proposal, have received constructive feedback on how to design and execute a viable study in the area of quantitative empirical financial accounting research.

 

Date:

Online: 03.09.24, 06.09.24, 10.09.24, 13.09.24, all slots 9am – noon.
Zoom: https://hu-berlin.zoom.us/j/69574538552

In person (FU Berlin): 18.09.24, 2pm – 20.09.24, noon

 

Location:

Online and in person

 

Course Language:

English

Lecturer:

Prof. Dr. Joachim Gassen (Humboldt-Universität zu Berlin, TRR 266 „Accounting for Transparency“)

http://www.wiwi.hu-berlin.de/professuren/bwl/rwuwp/staff/gassen

gassen@wiwi.hu-berlin.de

 

Registration:

Click for information on fees, payment and registration,
or email us: prodok@vhbonline.org.

Participation fee:

Non-member: 610,00 Euro
VHB-member: 490,00 Euro

Registration Deadline: 4. August 2024

VHB ProDok Kurse

Ethnographic Research

The course is designed for doctorate students in business administration who want to learn about and conduct ethnographic research. The aim of the course is to provide students with methodological foundations and advanced knowledge on ethnographic research in business studies. After attending this course, participants should be able to

  • understand the methodological foundations of ethnographic research
  • differentiate between different approaches to ethnography and be able to assess their strengths and weaknesses
  • plan an ethnographic research design as well as be prepared for methodological challenges in their field research
  • understand ways of analyzing ethnographic data and judge ethnographic research according to quality criteria

The first workshop day will provide an overview of the history of ethnography as well as introduce the participants to different approaches in ethnographic research. The second workshop day focuses on research ethics, quality criteria and publishing ethnographic research. On the third workshop day, the process of doing ethnographic research in the field as well as the issues of access, roles and forms of engagement will be discussed. The fourth day looks at different strategies for analyzing ethnographic data. The session will be hands-on and offer participants the opportunity to gain practical experience with analyzing ethnographic data.

Date:

September 2 – 5, 2024

 

Location:

Kyffhäuser 21
Kyffhäuserstr. 21
10781 Berlin

Course Language:

English

Lecturers:

Prof. Dr. Jana Costas,
Europa-Universität Viadrina Frankfurt (Oder)

Jana Costas is Professor of Business Administration, in particular People, Work and Management at the European University Viadrina Frankfurt (Oder). She holds a PhD from the University of Cambridge, and has been awarded with the EU Marie Curie Fellowship. She conducted the fellowship at the Copenhagen Business School. Jana has also been Assistant Professor (Juniorprofessorin) for Qualitative Methods in Management Research at Freie Universität Berlin. Her research interests lie in the area of organization studies, in particular secrecy, creativity, control, identity, culture, leadership, tech lobbying, violence, and new work and organizational arrangements. She has published in and reviews for various journals, such as Organization Studies, Journal of Management Studies, Human Relations. Jana is Associate Editor of Organization and acts on the Editorial Board of Organization Theory. She has published the monograph  Secrecy at Work: The Hidden Architecture of Organizational Life (with Chris Grey), Stanford University Press. Her ethnographic book Dramas of Dignity: Cleaners in the Corporate Underworld of Berlin published by Cambridge University Press won the EGOS book award 2023.

 

Prof. Blagoy Blagoev
Universität St.Gallen

Blagoy Blagoev is Professor of Organization Studies at University of St. Gallen (Switzerland). His research draws on a temporal lens to examine the interplay of people, organizations, and society in the context of current technological, ecological, and cultural transformations. His main research interests include (1) organizing and managing for sustainability, (2) emerging technologies and organizing, (3) new and decentralized forms of working and organizing, and (4) organizational change, innovation, and persistence. He draws on qualitative and historical research methods to examine a wide range of organizations such as multinational corporations, knowledge-intensive firms, museums, and coworking spaces. His work has appeared in leading international journals, such as Administrative Science Quarterly, Academy of Management Journal, Journal of Management Studies, Organization Studies, Organization and Scandinavian Journal of Management.

 

Prof. Dan Kärreman,
Copenhagen Business School

Dan Kärreman is Professor in Management and Organization Studies at Copenhagen Business School, and Professor in Business Administration at Lund University. His research interests include critical management studies, knowledge work, identity in organizations, leadership, organizational control and research methodology.  He received his PhD, based on an organizational ethnography, in 1997, and has held position at Gothenburg University, Lund University, Copenhagen Business School and Royal Holloway, University of London. His contributions to organizational methodology includes articles and books on organizational discourse analysis, mystery as method, theory creation and critical inquiry. He has contributed to more than 40 journal articles in peer review publications and has published in most top ranked journals in organization studies.

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: August 4, 2024

VHB ProDok Kurse

Advanced Topics in Organization Theory

This doctoral seminar exposes students to foundational and current research in organization theory. It is directed towards all business administration scholars interested in phenomena that involve organizations, which can be students of organization theory specifically, management and marketing more broadly, or even students of other areas of business research such as accounting, sustainability management or information systems for whom organizations, inter-organizational relationships and wider organizational and institutional fields might play a role in their research.

This course is not a basic course, however, but a course that focuses on current developments in organization theory. This does not necessarily mean that an in-depth prior knowledge of organization theory is required, but students should have a basic knowledge of the topic of organization and be familiar with some “classic“ organization theories such as the theory of bureaucracy, contingency theory or institutional theory.

After this course, participants will be able to:

  • Understand how classic organization theories have developed both theoretically and in terms of empirical research designs
  • Apply recent advances in organization theory to understand current organizational and inter­organizational phenomena
  • Develop relevant research questions that promise theoretical contributions to current (inter-) organizational thought

Date of Event:

September 16-19, 2024

Location:

Harnack-Haus
Ihnestraße 16-20
14195 Berlin

Lecturer:

Univ.-Prof. Dr. Elke SchüßlerLeuphana Universität Lüneburg

Prof. Dr. Jörg Sydow
Freie Universität Berlin

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

 

Registration Deadline: July 21, 2024

VHB ProDok Kurse

Machine Learning

The course exposes participants to recent developments in the field of machine learning and discusses their ramifications for business and economics. Machine learning comprises theories, concepts, and algorithms to infer patterns from observational data. The prevalence of data (“big data”) has led to an increasing interest in the corresponding methodology to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindset and ground to a large extent on machine learning. Familiarizing course participants with these concepts and enabling them to purposefully apply cutting-edge methods to real-world decision problems in management, policy development, and research is the overarching objective of the course. Accordingly, the course targets Ph.D. students and young researchers with a general interest in algorithmic decision-making and/or concrete plan to employ machine learning in their research. A clear and approachable explanation of relevant methodologies and recent developments in machine learning paired with a batterie of practical exercises using contemporary software libraries of (deep) machine learning will ready participants for design-science or empirical-quantitative research projects.

Date:

17. – 20. September 2024

Location:

Harnack-Haus Tagungsstätte der Max-Planck-Gesellschaft
Ihnestr. 16-20
14195 Berlin

The course will be offered over a four-day period comprising lecture, tutorial, and discussion sessions.

Course Language:

English

Lecturer:

Prof. Dr. Stefan Lessmann

Humboldt-Universität zu Berlin

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: 18. August 2024

VHB ProDok Kurse

Meta Analysis

Abstract and Learning Objectives

Meta-analyses have become very popular in many fields of the social sciences incl. business and management research. The results of meta-analyses attract substantial interest by both scholars and practitioners, as indicated by high citation numbers and widespread dissemination of meta-analytic findings in the media.

By summarizing results drawn from a set of studies concerning a specific topic and by discovering consistencies and explaining inconsistencies in these results, meta-analysis is an essential step in the process of knowledge accumulation, theory building and theory testing in science, linking past research with future scientific endeavors.

The course targets researchers who are interested in understanding, conducting, and publishing meta-analytic research. Participants of this course will learn how to conduct and publish a high-quality meta-analysis in the area of management and business research. To this aim, the course follows a step-by-step procedure that covers the entire meta-analysis research process, including problem formulation and definition of a research question for a meta-analysis, literature search, study and effects coding, data preparation and analysis with different software tools, and reporting and publishing. Participants will further learn how to evaluate meta-analyses in the business and management literature and to follow the respective methodological discussion about meta-analyses in their field.

Date:

September 10 – 13, 2024

Location:

Harnack-Haus
Ihnestr. 16-20
14195 Berlin

Language:

English

Lecturer:
Prof. Dr. Martin Eisend
European University Viadriana
Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: August, 11th, 2024

VHB ProDok Kurse

Machine Learning

The course exposes participants to recent developments in the field of machine learning and discusses their ramifications for business and economics. Machine learning comprises theories, concepts, and algorithms to infer patterns from observational data. The prevalence of data (“big data”) has led to an increasing interest in the corresponding methodology to leverage existing data assets for improved decision-making and business process optimization. Concepts such as business analytics, data science, and artificial intelligence are omnipresent in decision-makers’ mindset and ground to a large extent on machine learning. Familiarizing course participants with these concepts and enabling them to purposefully apply cutting-edge methods to real-world decision problems in management, policy development, and research is the overarching objective of the course. Accordingly, the course targets Ph.D. students and young researchers with a general interest in algorithmic decision-making and/or concrete plan to employ machine learning in their research. A clear and approachable explanation of relevant methodologies and recent developments in machine learning paired with a batterie of practical exercises using contemporary software libraries of (deep) machine learning will ready participants for design-science or empirical-quantitative research projects.

 

Date:

23. – 26. April 2024

Location:

Harnack-Haus Tagungsstätte der Max-Planck-Gesellschaft
Ihnestr. 16-20
14195 Berlin

The course will be offered over a four-day period comprising lecture, tutorial, and discussion sessions.

 

Course Language:

English

 

Lecturer:

Prof. Dr. Stefan Lessmann
Humboldt-Universität zu Berlin

Registration:

Click for information on fees, payment and registration,

or email us: prodok@vhbonline.org.

Registration Deadline: 24. März 2024

Doktorandenkurs “Umgang mit fehlenden Daten”

Kursname: Umgang mit fehlenden Daten

Informationen: Der Kurs ist interdisziplinär ausgerichtet und kann von allen Promovierenden besucht werden, die im Studium zumindest Grundkenntnisse klassischer multivariater Datenanalyseverfahren (lineare / generalisierte lineare Modelle) erworben haben. Der Fokus des Kurses liegt auf der praktischen Anwendung. Statistische Grundlagen (z.B. Markov Chain Monte Carlo) werden nur insoweit behandelt, wie sie zum Verständnis der Methode und für die adäquate Anwendung der jeweiligen Verfahren notwendig ist.

Dozent: Dr. Kristian Kleinke, Uni Siegen, Department of Psychology

Datum: Montag, 04.03.2024 und Montag, 18.03.2024

Uhrzeit: 9.00 – 17.00 Uhr

Kursformat: Online-Seminar

Anmeldung: Email an meisterc@hsu-hh.de bis zum 02.03.2024

Weitere Kursinformationen:

Ablauf:

Am ersten Kurstag wird eine umfassende Einführung in die Problematik fehlender Werte bei der Analyse sozialwissenschaftlicher Datensätze gegeben. Verschiedene Ansätze zur Behandlung fehlender Werte werden vorgestellt und deren Vor- und Nachteile diskutiert.

Der Fokus des zweiten Kurstages ist die praktische Anwendung. Teilnehmende sind herzlich eingeladen, hier auch eigene Daten mitzubringen und an eigenen kleineren Problemstellungen zu arbeiten.

Zusätzlich besteht im Anschluss an den zweiten Kurstag noch die Möglichkeit, die begonnene Arbeit an einer konkreten Problemstellung weiterhin beratend zu begleiten.

Inhalte:

Tag 1

– Missing-data-Muster und Mechanismen

– Diagnostik selektiver Missing-Data-Prozesse

– Überblick über verschiedene Ansätze zur Behandlung fehlender Werte (u.a., ad hoc Verfahren, maximum likelihood, Gewichtung)

– Eine Einführung in die (Bayes’sche) Multiple Imputation (MI)

 

Tag 2: Praktische Anwendung

– MI unter Annahme multivariat normal verteilter Daten

– MI basierend auf (generalisierten) linearen Modellen

– MI bei Paneldaten

– robustere MI-Ansätze (wenn parametrische Annahmen verletzt sind)

Voraussetzungen:

– Grundkenntnisse in klassischen multivariaten Verfahren (lineare Modelle, generalisierte lineare Modelle

– Wünschenswert, aber nicht notwendig: R-Grundkenntnisse

Vorbereitung:

Um während des Kurses Zeit zu sparen, installieren Sie bitte vorab folgende Software:

1) Blimp, kostenlos erhältlich unter https://www.appliedmissingdata.com/blimp

Sollten Sie im Rahmen Ihres Promotionsvorhabens auch Modelle für Zähldaten spezifizieren wollen, dann müssten Sie bitte auch zusätzlich die Beta-Version installieren.

2) eine aktuelle R-Version, erhältlich unter https://cloud.r-project.org (für die Installation einiger Zusatzpakete benötigt man ggf. unter Windows auch die “R-tools”, die ebenfalls unter dieser Seite erhältlich sind)

3) Optional: Eine aktuelle Version von Rstudio (Desktop), kostenlos erhältlich unter https://posit.co/download/rstudio-desktop/

Sofern Sie auf Ihrem (Dienst-)Rechner keine Administratorenrechte haben sollten, bitten Sie Ihre Systemadministratoren vorab, o.g. Software zu installieren.

Literatur:

Als Begleitlektüre zum Kurs / zur Vertiefung werden folgenden Bücher empfohlen

1) Van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). CRC.

Das Buch ist kostenlos online lesbar unter: https://stefvanbuuren.name/fimd/

2) Enders, C. K. (2022). Applied missing data analysis (2nd ed.). Guilford.

3) Kleinke, K., Reinecke, J., Salfrán, D., & Spiess, M. (2020). Applied multiple imputation. advantages, pitfalls, new developments and applications in R. Springer.