• 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

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).

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.