USING A SET-THEORETIC APPROACH FOR MORE PRECISE THEORY FOR MOTIVATION

Since qualitative research involves understanding context and social occurrences holistically, researchers will tend to think in terms of combinations and configurations. Researchers will often think of causal conditions in terms of “causal recipes”, the causally relevant conditions that combine to produce a given outcome. This interest in combinations of causes can also provide an explanation for “how” things happen. Therefore, a configurationally approach suggests that organizations are best understood as clusters of interconnected structures and practices, rather than as modular or loosely coupled entities whose components can be understood in isolation (Fiss, 2007, 1180). 

According to Ragin (2008), the challenge posed by configurationally thinking is to see causal conditions not as adversaries in the struggle to explain variation in dependent variables, but as potential collaborators in the production of outcomes. The key is not which variable has the biggest net effect, but how different conditions combine and whether there is only one combination or several different combinations of conditions (or causal recipes) capable of generating the same outcome. That is, a configurationally approach supports the idea that causation may be complex and that the same outcome may result from different combinations of conditions. Once these combinations are identified, it is possible to specify the contexts that enable or disable specific causes. Therefore, the configurationally approach takes a systemic and holistic view of organizational phenomena, where patterns and profiles rather than individual independent variables are related to performance outcomes (Delery & Doty, 1996; Drazin & Van de Ven, 1985). 

Early forms of configurationally approaches involved cluster analysis (e.g., Whittington, Pettigrew, Peck, Fenton, & Conyon, 1999). However, cluster analysis also has a number of limitations. For example, cluster analysis tends to treat each configuration as a “black box” insofar as only differences between constellations of variables can be detected. The analysis does not extend to the contribution of individual elements to the whole or to an understanding of how the variables combine to achieve the outcome (Ragin, 2000). This method also relies on research judgment to determine cutoff points for clustering, and results depend on the selection of the sample and variables, the scaling of the variables and the clustering method (Ketchen & Shook, 1996). 

Instead of using symmetric models with interaction effects or clustering algorithms, a set-theoretic approach uses Boolean algebra to determine which combinations of organizational characteristics combine to result in a specified outcome (Fiss, 2007). Qualitative Comparative Analysis (QCA) is often cited as an analytical approach and set of research tools to conduct detailed within-case analysis and formalized cross-case comparisons under the assumption of complex causality (Fiss, 2011; Legewie, 2013; Woodside, 2013). Complex causality means that: (1) causal factors combine with each other to lead to occurrence of a given type of phenomenon, (2) different combinations of causal factors can lead to the occurrence of a given type of phenomenon, and (3) causal factors can have opposing effects depending on the combinations with other factors in which they are situated (Wagemann & Schneider, 2010, 382). QCA’s sensitivity to causal complexity give it an analytic edge over many statistical techniques of data analysis (Schneider & Wagemann, 2010, 400).

Reference

• Delery, J. E., & Doty, D. H. (1996). Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configurational performance predictions.Academy of Management Journal, 39(4), 802–835. 
• Drazin, R., & Van de Ven, A. H. (1985). Alternative forms of fit in contingency theory. Administrative Science Quarterly, 30, 514–539. 
• Fiss, P. C. (2007). A set-theoretic approach to organizational configurations. Academy of Management Review, 32(4), 1180–1198. 
• Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420. 
• Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal, 17(6), 441–458. 
• Legewie, N. (2013). An introduction to applied data analysis with qualitative comparative analysis. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 14(3 (September)). 
• Ragin, C. C. (2000). Fuzzy-set social science. University of Chicago Press. 
• Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press. 
• Schneider, C. Q., & Wagemann, C. (2010). Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comparative Sociology, 9(3), 397–418. 
• Whittington, R., Pettigrew, A., Peck, S., Fenton, E., & Conyon, M. (1999). Change and complementarities in the new competitive landscape: A European panel study, 1992–1996. Organization Science, 10(5), 583–600. 
• Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463–472.