Search result: Catalogue data in Spring Semester 2019

Doctoral Department of Management, Technology, and Economics Information
More Information at: Link
Doctoral Studies in Management
NumberTitleTypeECTSHoursLecturers
364-0406-00LPublishing in Management, Technology and Innovation Restricted registration - show details
Limited number of participants.
Only 8 places are available for doctoral students from ETH (D-MTEC).

Registration: Students need to register via the email of the teaching assistant namely: Link (Zoe Jonassen).
The registration will be organized on the first come first served basis.
W2 credits1SG. von Krogh
AbstractThe seminar aims to improve the competence of doctoral students and post docs in the area of management, technology and innovation to publish their work in leading academic journals.
ObjectiveThe seminar adresses the following questions:
How to set up research for academic journals?
How to structure an academic paper for publication in selected journals?
How to adress editorial boards?
How to cope with editorial recommendations?
How to set up a publication strategy?
Target journals to be analysed are leading journals in the area of strategy, management, technology and innovation. Besides the journal analysis we will discuss selected papers in management and innovation research. This seminar will be conducted as a cooperation between EPFL, ETHZ and University of St. Gallen. Language is English.
Prerequisites / NoticeThe course takes place once a year in collaboration with HSG (Prof. Gassmann), EPFL (Prof. Tucci), and ETH (Prof. von Krogh).
This year's course will be held at ETH Zurich in June 2019.

Only 8 places are available for doctoral students from ETH, which are assigned on a first-come, first-served basis. You need to sign up by email to the teaching assistant (to be determined & announced here) to be registered for the course.
364-1020-01LMethods in Management Research: Methodological Fit in Management Research Information Restricted registration - show details
Number of participants limited to 15.
W1 credit1SJ. Schmutz
AbstractThis module covers basic issues of study design, such as definition of concepts/variables, choice of data collection and data analysis methods, validity and its limitations, and embedding research in existing paradigms/scientific communities.
ObjectiveThe module aims to support students in
- understanding the key elements of study design and the choices related to each
- knowing and being able to apply criteria for the validity of empirical research
- discussing methodological issues in relation to their own research
ContentBasic approaches to empirical inquiry (deduction, induction, abduction) and their relation to methodological perspectives (qualitative, quantitative, mixed) are discussed. Different types of validity of empirical research are introduced and applied to different methods for data collection and analysis. Consideration of levels of analysis and treatment of time are discussed as two additional key requirements in study design. The concepts introduced in the course are applied to pertinent examples of published research.
LiteratureSession 1:

Choices in study design and validity criteria
Scandura, T.A. & Williams, E.A. (2000). Research methodology in management: Current practices, trends, and implications for future research. Academy of Management Journal, 43, 1248-1264.

Edmondson, A.C. & McManus, S.E. (2007). Methodological fit in management field research. Academy of Management Review, 32, 1155-1179.

Creswell, J.W. (2009). Research design. Qualitative, quantitative and mixed methods approaches. Chap. 10: Mixed methods procedures.

Locke, K., Golden-Biddle, K. & Feldman, M.S. (2008). Making doubt generative: Rethinking the role of doubt in the research process. Organization Science, 19, 907-918.

Barley, S.R. (2006). When I write my masterpiece: Thoughts on what makes a paper interesting. Academy of Management Journal, 49, 16-20.

Brutus, S., Aguinis, H. & Wassmer, U. (2013). Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations, Journal of Management, 39, 48-75.

Schmutz, J. B., Lei, Z., Eppich, W. J., & Manser, T. (2018). Reflection in the heat of the moment: The role of in‐action team reflexivity in health care emergency teams. Journal of Organizational Behavior, 39(6), 749–765. (Example of quantitative research)

Bechky, B.A. & Okhyusen, G.A. (2011). Expecting the unexpected? How SWAT officers and film crews handle surprises. Academy of Management Journal, 54, 239-261. (Example of qualitative research)


Session 2: Considering levels of analysis and time in study design; Discussion of participants' "model papers"

Klein, K.J. & Kozlowski, S.W.J. (2000). Form Micro to Meso: Critical steps in conceptualizing and conducting multilevel research. Organizational Research Methods, 3, 211-236.

Mitchell, T.R. & James, L.R. (2001). Building better theory: Time and the specification of when things happen. Academy of Management Review, 26, 530-547.
Prerequisites / NoticeThere will be three assignments: (1) Prepare a written short summary and moderate discussion on one paper from course readings (1-2 persons); (2) Prepare short presentation of "model paper" focusing on your methods for your own research for general discussion (individually); (3) Read all course papers as basis for discussion in class.
364-1020-04LMethods in Management Research: Quantitative Research - Multilevel AnalysisW1 credit1SS. Raeder
AbstractMultilevel analysis is required for data collected in clustered samples for which sampling decisions were taken in several steps (e.g. first choosing firms, then employees in firms). The course provides basic knowledge about the design and analysis and some advanced applications such as models with three levels or with moderation and mediation.
ObjectiveThe course aims to support students in:
1) understanding multilevel design and statistics,
2) being able to design and calculate multilevel models,
3) being able to interpret and report the results of the statistics.
ContentThe course provides basic knowledge about the design and analysis of multilevel models and some advanced applications such as models with three levels or with moderation and mediation.

Session 1: Basics of multilevel modelling
- Statistical model,
- calculation of model in SPSS,
- required sample size,
- reporting of results.

Session 2:
- moderation and mediation in multilevel models,
- introduction to longitudinal analysis.

SPSS is used in the course for the practical course work.
Lecture notesPower points and all material used in the course are available in Moodle.
LiteratureSession 1:
Hox, J. (2010). Multilevel analysis. Techniques and applications (2nd ed.). New York: Routledge. Chapter 1& 2

Session 2:
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis. New York: Oxford University Press.
Hoffman, L. (2015). Longitudinal analysis. Modeling within-person fluctuation and change. New York, London: Routledge.

Further recommended reading:
Hox, J. (2010). Multilevel analysis. Techniques and applications (2nd ed.). New York: Routledge.
Snijders, T. A. B., & Bosker, R. J. (2011). Multilevel analysis (2nd ed.). An introduction to basic and advanced multilevel modeling. Sage: London.
Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. New York: Taylor & Francis.
Prerequisites / NoticeBasic knowledge in regression analysis is necessary for following the course.

Students work on three assignments:
1) before the course starts,
2) after session 1 and
3) after the end of the course.
Assignment 1 includes reading, finding a sample paper with multilevel analysis and providing information on experience with the method. More detailed information will be given before the course starts. Assignment 2 and 3 consist of analyzing data and reporting results.

It is expected that participants attend 100% of the teaching and work on all assignments.

SPSS is used in the course for practical course work. Students have to install the package on their computer.
364-1020-05LMethods in Management Research: Quantitative Research - Structural Equation ModellingW1 credit1SS. Raeder
AbstractStructural equation modeling (SEM) is a technique to build models and test causal relationships including latent variables, several outcome variables and intervening variables. The course provides basic knowledge about the design, analysis and reporting of structural equation models.
ObjectiveThe course aims to support students in:
1) understanding design and statistics of structural equation models,
2) being able to design and calculate a structural equation model,
3) being able to interpret and report the results of the statistics.
ContentThe course provides basic knowledge about the design and analysis of structural equation models.

Session 1: Basics of structural equation modeling and confirmatory factor analysis (CFA)
- model identification,
- model fit,
- measurement model and structural model,
- calculation of basic CFA and SEM model in Mplus.

Session 2:
- Calculation of more complex model (e.g. with intervening variables),
- reporting of results.

Mplus is used in the course for the practical course work.
Lecture notesPower points and all material used in the course are available in Moodle.
LiteratureRecommended reading:
Byrne, B. M. (2012). Structural Equation Modeling with Mplus. Basic concepts, applications, and programming. New York: Rutledge.
Geiser, C. (2012). Data Analysis with Mplus. Guildford: New York.
Geiser, C. (2011). Datenanalyse mit Mplus. Eine anwendungsorientierte Einführung. Springer VS: Wiesbaden.
Bühner, M. (2006). Einführung in die Test- und Fragebogenkonstruktion (2. Aufl.). München: Pearson. Kapitel 6
Prerequisites / NoticeBasic knowledge in regression analysis is necessary for following the course.

Students work on three assignments:
1) before the course starts,
2) after session 1 and
3) after the end of the course.
Assignment 1 includes finding a sample paper with SEM and providing information on experience with the method. More detailed information will be given before the course starts. Assignment 2 and 3 consist of analyzing data and reporting results.

It is expected that participants attend 100% of the teaching and work on all assignments.
364-1020-06LMethods in Management Research: Experimental ResearchW1 credit1SP. Schmid
AbstractThis course teaches the basics of experimental research methods. The most important steps in conducting an experiment will be discussed.
ObjectiveStudents will learn how to design their own experiment and will become aware of the most important factors that need to be considered when planning and executing experimental research. Specifically, it will be discussed how to formulate research questions and hypotheses, how to operationalize the relevant concepts, how to construe the experimental design, how to control potential confounding variables, how to determine the sample size, and how to carry out the experiment. As part of the course, students will design their own experiment and present it in class. Moreover, students will be given a scientific article that includes experimental research and will be asked to discuss the strong and weak points of the experimental design in class. This exercise will train students' critical thinking about scientific evidence.

This course focuses primarily on laboratory and online research; however many aspects can be applied to field experiments as well.
LiteratureSuggested method books (good reference books)

Research Methods in Psychology: Investigating human behavior. (2nd edition) P. G. Nestor, & R. K. Schutt (Eds.), SAGE

Research Methods in Psychology (4th edition) G. M. Breakwell, J. A., Smith, & D. B. Wright (Eds.), SAGE
364-1020-07LQualitative Methods for Management Studies Restricted registration - show details
Does not take place this semester.
Number of participants limited to 15.
W3 credits2GS. Brusoni
AbstractThis course addresses the main problems related to design, implementation and publication of qualitative research on generalist management journals.
ObjectiveAt the end of the course students will be able to define what qualitative methods are, compare and differentiate the methods’ relative advantages, design and implement a data collection process, and analyze qualitative data.
ContentParticipation in all sessions is a requirement.
364-1052-00LPhD Seminar in Quantitative Marketing Research Restricted registration - show details W3 credits1SF. von Wangenheim, R. Algesheimer
AbstractThe seminar is open to PhD students in Quantitative Marketing. Students are invited to present "work in progress". Work to be presented should be in a state that allows for submission to an international peer-reviewed journal in the not too distant future. This seminar is a collaboration between ETH and UZH and marketing groups from both sides will participate.
ObjectiveThe learning goal of the course is to reflect on and improve participants' research skills through presentation and discussion of research in progress projects.
To be prepared for the seminar, students need to read up on central topics in the related literature. These references are listed in the forthcoming syllabus. Students are invited to present "work in progress".
ContentThe seminar is open to PhD students in Quantitative Marketing. To be prepared for the seminar, students need to read up on central topics in the related literature. These references are handed out in the beginning of the seminar. Students are invited to present "work in progress". Work to be presented should be in a state that allows for submission to an international peer-reviewed journal in the not too distant future. This seminar is a collaboration between ETH and UZH and marketing groups from both sides will participate.
To be prepared for the seminar, students need to read up on central topics in the related literature. These references are listed in the forthcoming syllabus. Students are invited to present "work in progress".
364-1119-00LNext-Generation Information Systems Restricted registration - show details
Number of participants limited to 10.
W1 credit1SS. Feuerriegel, E. Fleisch
AbstractThis seminar will explore recent advances in the areas of information systems and business analytics with a particular focus on quantitative research. An essential aspect of any research project is dissemination of the findings arising from the study.
ObjectiveThe seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills.
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