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2023/2024  KAN-CGMAO2004U  Qualitative Methods and Reasoning

English Title
Qualitative Methods and Reasoning

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for cand.merc. and GMA (GMA)
Course coordinator
  • Rasmus Koss Hartmann - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Methodology and philosophy of science
Teaching methods
  • Blended learning
Last updated on 06-02-2023

Relevant links

Learning objectives
At the end of the course,
  • Be able to select qualitative methods relevant to particular business and management problems and decision situations, and justify the contextual relevance of the selected method
  • Master the use of qualitative scientific methods within business economics and management, including rigorous approaches to data collection, data analysis, interpretation and conceptualization
  • Rigorously reflect on the implications of knowledge problems and participation for the qualitative research process
  • Communicate the process and findings of qualitative analysis in clear, coherent, written arguments
Qualitative Methods and Reasoning:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Written assignment
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) Internal examiner and external examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

The course exam would be an individual ten-page (4000 words) assignment where students collect, interpret, present and reflect on qualitative data relevant to a self-chosen case.

Course content, structure and pedagogical approach

As befits its focus on data analytics, the program as a whole generally emphasizes the quantitative methods that data analytics is typically taken to imply and posits that informed decision making often and ideally means reliance on the kind of ‘big’ and ‘hard’ data frequently associated with it. Naturally, the program also emphasizes the limitation of quantitative methods, both epistemically and practically: there are certain things that quantitative methods will never do, certain things they will never do well or without substantive risks, errors and implications, and certain things that they can only do at great cost. Naturally, this leaves open the question how of those things might nonetheless be made ‘knowable’ and that is what this course addresses. This is the stuff of qualitative methods and low-n reasoning.

The course focuses on four forms of data collection: analytical interviews, ethnographic market insight, auto-ethnographic organizational insight, and vicarious learning. All four represent low-cost and low-n ways of generating insights into business processes that in keys ways complement what can be achieved with what we typically refer to as ‘data analytics’.

Analytical interviews a logical progression on a widely used qualitative technique deployed in bachelor programs, namely semistructured interviews. Analytical interviews bring the act of interpretation into the interview situation and using the interview as an occasion to not just collect data, but to develop explanations. This is in itself a useful starting point to understand the dynamics “around” data and understanding in organizations, but also serves as a exemplification of how interpretation and iteration are central to the qualitative research process.

As regards market insights, it is well established that there will often be situations where organizations find themselves missing analyzable data, where conventional sampling techniques cease to be relevant, and customer understandings of their own future needs may be immensely limited. In these situations, ethnographically-informed studies, done either ‘on the ground’ or online, can provide insights into the customer needs and behavior that can be essential to serving those customers well. 

For organizational insights, it is equally clear that managers often have to act in circumstances where it is simply unpractical to try to use quantitative data to assess problems. Organizational situations may be so complex and ambiguous that quantitative methods deepen the knowledge problems that managers face, or the variables of interest may be such that they can only be captured very poorly with quantitative methods. In such circumstance, auto-ethnographic methods provide a way for managers to become attentive to and reflexive about the organizational dynamics in which they partake.

As regards vicarious learning, it is – for all the uniqueness and idiosyncrasies of individual organizations – evident that organizations can learn from the experience of other organizations. A general manager will often have to make decisions that are not repeatable, that are reversible only at great cost, and that may entail extensive follow-on (“unintended”) consequences. Implementation of new-to-the-organization technologies or management concepts, or strategic changes, are examples of this. Under such circumstances, drawing inferences from samples of less than one becomes a central capability.

Common to all of these forms of data is a set of challenges: how do you analyze data, how does analysis become interpretation, how do you make the “leap” from interpretation to conceptualization, and how do you – across these processes – relate to complex data in which you yourself are complicit? Data analysis begins from a systematic, in-depth engagement with the data, which might be structured by particular methodological protocols, and often happens concurrently with data collection. Interpretation, especially when dealing with low-n data, involves developing multiple interesting explanations on the basis of data, and subsequently interrogating and selecting between those explanations. Moving from interpretation to conceptualization involves connecting one’s interpretations to theory, and recursively bringing theory to bear in refining and evaluating interpretations. Relating to data requires reflexivity about one’s own assumptions, one’s role in the creation and interpretation of data, and the persistent challenges that complexity, ambiguity and uncertainty might represent to understanding managerial problems.


Course Format

The course treats each of these instances of ‘low-n’ reasoning – market ethnography, organizational auto-ethnography, vicarious learning – in turn, building on students’ prior knowledge about qualitative methods like field observations and interviews and extending them into the more sophisticated practice of systematic qualitative inference with the complications that (i) decision situations can be beset by knowledge problems and (ii) that observers may be complicit to the problems they are studying and their data therefore “interested”. This progression is reflected in each of the three foci, but also in the course’s attention to analysis and low-n reasoning. Across all three applications, the informed management decision requires not just qualitative data but also that this qualitative data be analyzed, interpreted and conceptualized with a mindfulness of the biases, distortions and self-deceptive tendencies that are endemic to efforts to study one’s own practice. To bring about this mindfulness, the course is going to focus on both the methodological tools that might allow students to engage with low-n, interested data and on the organizational circumstances that might provide room and opportunity for this kind of data to be usefully applied (Hartmann et al, 2022).

Description of the teaching methods
The course is going to be organized as a series of lectures that describe and exemplify particular methods, co-taught by the course coordinator and members of a teaching team comprising both technical experts on the methods involved, practitioners of the methods, and practicing managers. Lectures will be complemented by workshops, some of which will be opportunities for students to present their group assignments. Lectures are tentatively expected to cover:

i) The relevance of qualitative methods in decision making, and complementarities between methods
ii) The organizational conditions for qualitative analysis
iii) Interpretation and “models”
iv) Analytical interviews
v) Ethnographic market data
vi) Auto-ethnographic organizational data
vii) Vicarious learning
viii) Analyzing qualitative, low-n data
ix) Conceptual leaps, gaps and mysteries
x) Methodological and methodical reflexivity
Feedback during the teaching period
Students will have opportunities to receive feedback through in-class activities, participation and presentation in workshops, and formally organized peer feedback activities.
Student workload
Participation in lectures 40 hours
Participation in workshops 10 hours
Preparation and out-of-class exercises 106 hours
Exam 50 hours
Expected literature

A list of relevant literature will be provided in class. Below please find an indicative literature:

Arnould, E. J., & Wallendorf, M. (1994). Market-oriented ethnography: interpretation building and marketing strategy formulation. Journal ofMarketingResearch, 31(4), 484-504.

Hartmann, R.K., Kärreman, D., Meier, N., Hauberg, T.M., & Ingerslev, K. (2022). Craft, Reflexivity and the Clinical Practice of Management, SSRN.

Lave, C. A. & March, J. G. (1993) An introduction to models in the social sciences. University Press of America.

Kärreman, D., Spicer, A., & Hartmann, R. K. (2021). Slow management. Scandinavian Journal of Management, 37(2), 101152.

Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61-72.

Kreiner, K. & Mouritsen, J. (2006) The analytical interview. In: Tengblad, Solli & Czarniawska (eds.) The art of science. Liber.

March, J. G., Sproull, L. S. & Tamuz, M. (1991) Learning form samples of one or fewer. Organization Science. 2(1).

Schouten, J. W., & McAlexander, J. H. (1995). Subcultures of consumption: An ethnography of the new bikers. Journal of consumer research, 22(1), 43-61.

Townsend, D. M., Hunt, R. A., McMullen, J. S., & Sarasvathy, S. D. (2018). Uncertainty, knowledge problems, and entrepreneurial action. Academy of Management Annals, 12(2), 659-687.

Von Hippel, E., Franke, N., & Prügl, R. (2009). Pyramiding: Efficient search for rare subjects. Research Policy, 38(9), 1397-1406.

Last updated on 06-02-2023