Search result: Catalogue data in Autumn Semester 2021
|Doctoral Department of Management, Technology, and Economics |
More Information at: https://www.ethz.ch/en/doctorate.html
|Doctoral Studies in Management|
|364-1013-05L||Organizational Behavior |
Does not take place this semester.
Number of participants limited to 20.
|W||1 credit||1S||to be announced|
|Abstract||Organizational behavior concerns the study of individual and group-level processes in organizations like creativity, motivation, and leadership. In this PhD course, an overview of major concepts and research insights in organizational behavior is provided. The participants are encouraged to discuss their own work situation as PhD students in relation to the OB insights covered in the course.|
|Objective||The objectives of the course are: |
• to provide an overview of OB research
• to discuss major research streams in OB
• to enable students to reflect their own work situation based on concepts used in OB.
|364-1013-06L||Marketing Theory |
Number of participants limited to 18.
|W||2 credits||1G||F. von Wangenheim|
|Abstract||The course is taught Florian Wangenheim (ETHZ)|
It focuses on the theoretical foundations of marketing and marketing research.
|Objective||The purpose of the course is to confront students with current theoretical thinking in marketing, and currently used theories for understanding and explaining buyer and customer behavior in reponse to marketing action.|
|Content||In the first class, current understanding of the marketing literature and marketing thought is discussed. |
In the following classes, various theories are discussed, particularly in light of their importance for marketing. Economic, pschological and sociological theory will be related to current marketing thought.
|364-1110-00L||Foundations of Innovation Studies||W||3 credits||2G||S. Brusoni, D. Laureiro Martinez|
|Abstract||This course will introduce some of the major theoretical threads and controversies in the broad field of innovation. During the first part of the course, the emphasis will be on the evolution of innovation studies. The final part of the course will focus on one of the directions in which those studies have evolved: the field of managerial cognition.|
|Objective||Students will learn about various perspectives, examine different methodologies, explore some original empirical research, make connections between theory and empirical research, and practice reviewing and identifying insight in research.|
1) Be able to display some knowledge on a few major theoretical streams in the area.
2) Be familiar with the methods, issues and current gaps in the area.
3) Have practiced skills in finding insight and reviewing the literature.
4) Have practiced skills in defining research problems and proposing empirical research in this area.
|364-0553-00L||Innovation in Digital Space |
Does not take place this semester.
|W||1 credit||1G||G. von Krogh|
|Abstract||The purpose of this course is to review and discuss issues in current theory and research relevant to innovation in the digital space.|
|Objective||Through in-depth analysis of published work, doctoral candidates will identify and appraise theoretical and empirical studies, formulate research questions, and improve the positioning of their own research within the academic debate.|
|Content||The Internet has a twofold impact on the way individuals and firms innovate. First, firms increasingly draw on digital technology to access and capture innovation-relevant knowledge in their environment. Second, individuals, firms, and other organizations extensively utilize the Internet to create, diffuse, and commercialize new digital products and services. During the past decade, theory and research on innovation in the digital space has flourished and generated extensive insights of relevance to both academia and management practice. This has brought us better understanding of working models, and some fundamental reasons for innovation success or failure. A host of new models and research designs have been created to explore the innovation in the digital space, but these have also brought out many open research questions. We will review some of the existing streams of work, and in the process explore a new research agenda. |
The course is organized in one block of 2 days. The course is a combination of pre-readings, presentations by faculty and students, and discussions. The students prepare presentations of papers in order to facilitate analysis and discussion.
|Literature||Open source (OS) as innovation model|
1. Lerner, J., & Tirole, J. (2002). Some Simple Economics of Open Source. JIE
2. von Hippel, E., & Von Krogh, G. (2003). Open source software and the 'private-collective' innovation model: Issues for Organization Science. OrgSci
3. von Krogh, G., Spaeth, S., & Lakhani, K. R. (2003). Community, joining, and specialization in open source software innovation: A case study. RP
4. Lakhani, K., & Eric, A. (2000). von Hippel (2003),“How open source software works:" free" user-to-user assistance”. RP
5. Yoo, Y., Boland, R. J., Lyytinen, K., & Majchrzak, A. (2012). Organizing for Innovation in the Digitized World. OrgSci
Coordination in OS communities
6. Faraj, S., von Krogh, G., Monteiro, E., & Lakhani, K. (2016). Special Section Introduction - Online Community as Space for Knowledge Flows. ISR
7. Lindberg, A., Berente, N., Gaskin, J., & Lyytinen, K. (2016). Coordinating interdependencies in online communities: A study of an open source software project. ISR
8. Shaikh, M., & Vaast, E. (2016). Folding and unfolding: Balancing openness and transparency in open source communities. ISR
9. Ren, Y., Chen, J., & Riedl, J. (2016). The impact and evolution of group diversity in online open collaboration. ManSci
10. Jiang, Q., Tan, C. H., Sia, C. L., & Wei, K. K. (2019). Followership in an Open-Source Software Project and its Significance in Code Reuse. MISQ
11. Medappa, P. K., & Srivastava, S. C. (2019). Does Superposition Influence the Success of FLOSS Projects? An Examination of Open-Source Software Development by Organizations and Individuals. ISR
12. Howison, J., & Crowston, K. (2014). Collaboration through open superposition: A theory of the open source way. MISQ
Governance & Leadership
13. He. F., Puranam P., Shrestha Y. R., & von Krogh, G. (2020) Resolving governance disputes in communities: A study of software license decisions. SMJ
14. Gulati, R., Puranam, P., & Tushman, M. (2012). Meta-organization design: Rethinking design in interorganizational and community contexts. SMJ
15. Fjeldstad, Ø. D., Snow, C. C., Miles, R. E., & Lettl, C. (2012). The architecture of collaboration. SMJ
16. Klapper, H., & Reitzig, M. (2018). On the effects of authority on peer motivation: L earning from Wikipedia. SMJ
17. Johnson, S. L., Safadi, H., & Faraj, S. (2015). The emergence of online community leadership. ISR
18. Safadi, H., Johnson, S. L., & Faraj, S. (2020). Core-Periphery Tension in Online Innovation Communities. OrgSci
19. Germonprez, M., Kendall, J. E., Kendall, K. E., Mathiassen, L., Young, B., & Warner, B. (2017). A theory of responsive design: A field study of corporate engagement with open source communities. ISR
20. Greenstein, S., & Zhu, F. (2016). Open content, Linus’ law, and neutral point of view. ISR
21. Nagle, F. (2019) Open source software and firm productivity. ManSci
22. Fitzgerald, B. (2006). The transformation of open source software. MISQ
Motivation to collaborate
23. Spaeth, S., von Krogh, G., & He, F. (2015). Perceived Firm Attributes and Intrinsic Motivation in Sponsored Open Source Software Projects. ISR.
24. Shah, S. K. (2006). Motivation, governance, and the viability of hybrid forms in open source software development. ManSci
25. von Krogh, G., Haefliger, S., Spaeth, S., & Wallin, M. W. (2012). Carrots and rainbows: Motivation and social practice in open source software development. MISQ
26. Hwang, E. H., Singh, P. V., & Argote, L. (2015). Knowledge sharing in online communities: Learning to cross geographic and hierarchical boundaries. OrgSci
27. Bapna, S., Benner, M. J., & Qiu, L. (2019). Nurturing Online Communities: An Empirical Investigation. MISQ
28. Goes, P. B., Guo, C., & Lin, M. (2016). Do incentive hierarchies induce user effort? Evidence from an online knowledge exchange. ISR
|364-1140-00L||Hacking for Social Sciences - An Applied Guide to Programming with Data |
Basic experience with either R or Python, e.g., a stats course that was taught using R.
|W||3 credits||2V||M. Bannert|
|Abstract||The vast majority of data has been created within the last decade. As a result, more and more fields of research start to consider and embrace programming to process and analyse data. This course teaches applied programming with data and aims to leverage the open source tech stack to deal with this new wealth and complexity of data.|
|Objective||The idea behind Hacking for Social Sciences is build a solid understanding of core technologies and concepts to help researchers develop a data processing strategy and increase your possibilities when working with data. The course approach is to single out those concepts stemming from software development that are easy to adopt and useful to social scientists. The course has three major learning objectives:|
- Understand the role of focal components in a data science tech toolbox.
Learn how technologies like R, Python, Git Version Control, docker or Cloud Computing could play together in your research project.
- Learn how to manage and version control source code.
Hacking for Social Sciences teaches how to use git version control to collaborate professionally, make your research reproducible and your code base persistent.
- Applied data sourcing and data transformation
Learn how to communicate with SQL databases. Learn how to consume data from different sources using machine to machine communication interfaces (APIs) such as the OpenStreetMap geocoding API / Routing Engine or the KOF data API for macroeconomic time series.
Hacking for Social Sciences is not a Statistics, Econometrics or Machine Learning course. Though experience in these fields will help inasmuch that students will have an easier time to motivate investing in programming and to come up with their own application examples, profound methodological knowledge is not a prerequisite.
|Content||Hacking for Social Scientists is a guide to programming with data. It is tailored to the needs of a field in which scholars’ typical curricula do not contain a strong programming component. Yet this course argues that what the open source community calls a ‘software carpentry’ level is totally within reach for a quantitative social scientist and well worth the investment: being able to code leverages field specific expertise and fosters interdisciplinary collaboration, as source code continues to become an important communication channel. |
The course contains three blocks that are mostly based on the three learning objectives presented above. Hacking for Social Sciences explicitly plans to spread its three blocks over 1-2 months to give students the ability to work on applied examples in between sessions in order to get most out of the subsequent session.
The first block demonstrates the components of a modern data science tech stack, classifies technologies and gives a big picture overview: from languages such as R and Python to container technology such as docker. The second block focuses on git version control, the de facto industry standard to manage source code. Version control is not only crucial to knowledge management and reproducible research, but it is also the backbone of collaboration in distributed teams. The third and final block focuses on data themselves
and teaches how to obtain data through machine to machine communication. Furthermore, the third block discusses data management in a research project.
|Lecture notes||A free and open online book (made with bookdown) is available from https://h4sci.github.io/h4sci-book/. The book/script will be continuously updated during the course to account for questions and participants' questions. |
All course materials including, slides, resources and source code will be made available through: https://h4sci.github.io/
|Literature||A free and open online book (made with bookdown) is available from https://h4sci.github.io/h4sci-book/. The book/script will be continuously updated during the course to account for questions and participants' questions. |
All course materials including, slides, resources and source code will be made available through: https://h4sci.github.io/
|Prerequisites / Notice||Basic experience with either R or Python, e.g., a stats course that was taught using R.|
|364-1013-02L||Perspectives on Organizational Knowledge||W||1 credit||1G||Z. Erden Özkol|
|Abstract||This module aims to introduce major theoretical perspectives on organizational knowledge and to improve the competence of doctoral students to publish in relevant research areas. How knowledge is conceptualized and what aspects of knowledge are being studied depends on the epistemological and ontological assumptions accepted by researchers.|
|Objective||This module aims:|
· to provide a basic understanding of key theoretical perspectives on organizational knowledge.
· to provide insights on the research questions, methods, findings and implications of the selected papers.
· to build skills in critically analyzing the literature.
· to identify future directions in the area.
|Content||Given its prominence in the history of organization science, an impressive variety of theories have evolved that deals with organizational epistemology, the way of knowing in the organization (e.g., Brown & Duguid, 1991; Grant, 1996; Kogut & Zander, 1992; Lave & Wenger, 1991; Nonaka, 1994; Spender, 1996; Tsoukas, 1996; von Krogh et al., 1994). In this module, students will learn about various seminal contributions in the area of organizational knowledge and make connections between theory and empirical research, and identify the ongoing trends and future research directions.|
Session 1: Knowledge based view of the firm.
Session 2: Knowledge sharing and transfer
Session 3: Social practice view on knowledge and knowing
|Literature||Remark: The list might change. Students will be informed about the changes before the first session. |
- von Krogh G, Roos J, Slocum K. 1994. An essay on corporate epistemology. Strategic Management Journal, Summer Special Issue 15: 53-71.
- Nonaka, I., 1994. A dynamic theory of organizational knowledge creation. Organization Science 5: 14-37.
- Kogut, B., Zander, U., 1992. Knowledge of the firm, combinative capacities and the replication of technology. Organization Science 3: 383-397.
- Grant, R. M. 1996. Toward a knowledge-based theory of the firm. Strategic Management Journal, 17: 109-122.
- Spender, J.-C. 1996. Making knowledge the basis of a dynamic theory of the firm. Strategic Management Journal, 17: 45-62.
- Szulanski, G. 1996. Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17: 27-43.
- Osterloh, M. and B. Frey, 2000. Motivation, Knowledge Transfer and Organizational Forms, Organization Science, 11: 538-550.
- Carlile, Paul Reuben. 2002. A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science 13 442-455.
- Hansen, M. T. 1999. The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Admin. Sci. Quart. 44 82-111.
- DeCarolis, D.M., D.L. Deeds. 1999. The impact of stocks and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal. 20(10) 953-968.
- Brown JS, Duguid P. 2001. Knowledge and organization: a social practice perspective. Organization Science. 12: 198-213.
- Cook SDN, Brown JS. 1999. Bridging epistemologies: the generative dance between organizational knowledge and organizational knowing. Organization Science. 10(4): 381-400.
- Orlikowski, W. J. 2002. Knowing in practice: Enacting a collective capability in distributed organizing. Organization Science, 10: 249-273.
- Nicolini, D. 2011. Practice As The Site Of Knowing: Insights From The Field Of Telemedicine. Organization Science. 22 (3): 602-620.
- Ewenstein, B. & Whyte, J. 2009. Knowledge practices in design: The role of visual representations as 'epistemic objects'. Organization Studies, 30, 7-30.
|Prerequisites / Notice||In each session, students will have three assignments: |
1) prepare for in-depth discussion of all papers. The students are supposed to read in advance all the papers that will be presented in the sessions.
2) critically review and discuss the assigned papers. Assignments will be done after participants confirm their presence.
3) submit in advance a short critique of the assigned papers - max 2 pages.
- Page 1 of 1