AOM 2020: Qualitative Analysis Boot Camp V: Theoretical Hunches and How to Theorize from Data
Professional Development Workshop on 7 August 2020
1. While we write and read the paper in a linear fashion, the qualitative, inductive study does not typically occur like that. It is a constant iterative process between data and literature review/contribution. There is a back and forth movement between theory and data until you refine your contribution.
2. You may start the field work with certain questions in mind and expectations to these answers. In the field, you may find insights that conform to your expectations and some things that are interesting, which are out of your area of expected findings or an area you did not even think about when setting up this study. Follow your hunch that is theoretically informed and seem to suit the research question – play around this hunch and explore the data by comparing and contrasting the situations, etc.
3. You may find that your expected finding is not the core of the data. Rather, playing around “why this is weird”, “that surprises me”, “why does it happen?”. Your hunch may be a dud – for example, you expected to see this finding, but you did not find it in the field. Don’t give up on this and think about why this is so. The fact that you had this hunch that was theoretically informed and should have occurred but did not, is already interesting and valuable. It means there is a better answer and you just need to dig deeper.
4. This is how you know that you have not made a theoretical contribution – when you are writing the contributions section and you highlight that “My findings are consistent with / are the same as XX …”. When you are not consistent with everyone else, there is a possibility that you found something to contribute to (or perhaps you just have not read the literature enough). Possible terms that signal your contribution includes “contrary to”, “elaborating a point”, and “counterintuitive to others”. Further develop your findings and test your hunch – go back and forth between findings and theory until you work out a compelling story and how does that address some problem in the literature.
5. Qualitative research provides that opportunity to discover inconsistent findings with theory or existing studies. Usually in reality, some part of your findings is consistent and linking with existing literature – which is good as it means your analyses is developing – and your hunch is not wrong. However, you need to go deeper in the analyses and go beyond the surface that is consistent with others. Go deeper into your analyses to see what you are different from existing literature, what is the nuance in your study. Ask yourself, what explains what I see, why did the shift happen? Do it empirically first, then go back to the literature to see if someone has already studied. One way to carve out a contribution is to go deeper into “why”, “when”, and “how” of what people have already found and how your data might explain it.
6. Work towards an explanation of the process, not just describing the pattern emerging from your data. Then think about what are the key constructs or concepts that explain the process, then go to the literature for each of the key constructs to better understand. Get specific with each constructs and mechanisms and study them in detail. Pick out which part of the literature for the key constructs that are valuable for your study. Then you can know what is said and what is different in your data.
7. A mistake that junior scholars make is to work on the data without first referring to the literature – you will never code your way to a contribution. First look at the literature and identify the gap, inform your coding, you have your hunch and ah-ha moment, then backtrack why is this gap important?
When you are empirically present, start writing little notes that makes you connect with theoretically what is important and interesting. Write in bullet point upon returning from the field to force yourself to think (1) this is what is happening in the field, and (2) why is this interesting. It forms a base on your working theory of the data – you may not have the theoretical language for it at the moment. At this point, work out why what you have appears novel.
8. Develop a findings (y-axis) – contributions (x-axis) table. Write down (1) the key findings of your study, and (2) what are we learning here and what literatures are you contributing to. This helps with developing coherence of your findings. Build your tables and write around them.
1. I have benefited from this PDW so much, as I am struggling with bridging between my empirical findings and developing a theoretical contribution. To me, this is a tacit knowledge and skill – it has to be caught rather than taught. Obviously, I think many of us know that we have to have a theoretical contribution, we have to dig deeper in the data, we have to reiterate between data and theory – but how does this look like, what do you look out for, what are some thought processes to have when you go through the data – all these usually are not spelt out. Experienced qualitative researchers will find this so intuitive but junior scholars are still trying to figure this process out. Having panelists with experience spell it out explicitly how they do it is so helpful – having guideposts (e.g. this is how you know you are NOT having a contribution, this is how you make a contribution) is valuable.
2. I really appreciate the panelists’ candid sharing of how they engage with the data-theory process. I realized this much later in my PhD journey that while the theory books say you need to look at the data first, it is inductive, not to be overly informed by the literature etc, we actually need to first read and be thoroughly informed of the literature before going into the field and doing our data analyses. Like what one panelist shared, we can never code our way to a contribution. Find out what is said and done, then analyze your data to find out what is different, what is a nuance, can you explain a process/mechanism of what people found.
1. Carlsen & Dutton (2011) Research Alive: Exploring Generative Moments in Doing Qualitative Research, Advances in Organization Studies, Copenhagen Business School Press.
2. Le & Schmid (2020) The practice of innovative research methods. Organizational Research Methods (Advance online publication)
3. Kohler, Smith, Bhakoo (2018) Feature Topic for ORM: “Templates in Qualitative Research Methods” Organizational Research Methods, 22 (1), 3-5.
