In a current manuscript^1, we embrace an abductive approach, which allows for theory refinement other than new theory generation or theory testing (Dubois & Gadde, 2002). Abduction can be appropriate when a phenomenon has a high degree of novelty and the aim is to investigate the underlying variables and their relationships (Coffey & Atkinson, 1996). Abductive reasoning is logically located somewhere inbetween inductive and deductive reasoning. But what are the actual differences of these reasonings and when should which approach be applied in academic research? Let us have a brief look:
What is deductive reasoning?
Deductive reasoning is a type of logical thinking that starts with a general statement or hypothesis and narrows down to a specific conclusion. In deductive research, a researcher begins with a theory or hypothesis and then collects and analyzes data to test that hypothesis. If the data support the hypothesis, the researcher can then use deductive reasoning to draw a conclusion. For example, a researcher may have a hypothesis that all birds can fly. To test this hypothesis, the researcher could collect data on a variety of different bird species and observe whether or not they are able to fly. If the data support the hypothesis, the researcher could then use deductive reasoning to conclude that all birds can fly.
What is abductive reasoning?
Abductive reasoning is a type of logical thinking that involves coming up with the best explanation for a set of observations. In abductive research, a researcher begins with a set of observations and uses them to generate a hypothesis or explanation. This explanation is then tested through further observation and experimentation. For example, a researcher may observe that a particular species of plant always grows in a certain type of soil. Based on this observation, the researcher may generate a hypothesis that the plant requires that type of soil in order to grow. The researcher could then test this hypothesis by growing the plant in different types of soil and observing whether or not it grows.
What is inductive reasoning?
Inductive reasoning is a type of logical thinking that involves moving from specific observations to broader generalizations and theories. In inductive research, a researcher begins with a set of observations and uses them to generate a theory or explanation. This explanation is then tested through further observation and experimentation. For example, a researcher may observe that a particular type of bird always builds its nest in a certain way. Based on this observation, the researcher may generate a theory that all birds of that species build their nests in that way. The researcher could then test this theory by observing other birds of that species and seeing if they also build their nests in the same way.
Overall, these three types of reasoning are commonly used in academic research to test hypotheses, generate explanations, and develop theories. They are useful tools for helping researchers to think logically and systematically about their data, and to draw accurate and reliable conclusions from that data.
If you want to undertake qualitative research (e.g., using data from expert interviews), I highly recommend the following literature as a primer:
Coffey, A. J., & Atkinson, P. A. (1996). Making Sense of Qualitative Data - Complementary Resesearch Strategies. SAGE Publications Inc., https://us.sagepub.com/en-us/nam/making-sense-of-qualitative-data/book5617
Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Technics and procedures for developing grounded theory (3rd ed.). SAGE Publications. https://doi.org/10.4135/9781452230153
Dubois, A., & Gadde, L.-E. (2002). Systematic combining: an abductive approach to case research. Journal of Business Research, 55(7), 553–560. https://doi.org/10.1016/S0148-2963(00)00195-8
Reichertz, Jo. (2007). Abduction: The Logic of Discovery of Grounded Theory. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research. 11. 10.4135/9781848607941.n10.
- As of March 2022, the manuscript has been published in the Journal Business Strategy and the Environment under
Böhmecke-Schwafert, M., Wehinger, M., & Teigland, R. (2022). Blockchain for the circular economy: Theorizing blockchain’s role inthe transition to a circular economy through an empirical investigation.Business Strategy and the Environment,1–16.https://doi.org/10.1002/bse.3032
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