Necessary Condition Analysis
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Business and Management. Please check back later for the full article.
Necessary Condition Analysis (NCA) understands cause-effect relations as “necessary but not sufficient.” It means that without the right level of the cause a certain effect cannot occur. This is independent of other causes, thus the necessary condition can be a single bottleneck, critical factor, constraint, disqualifier, or the like that blocks the outcome. This logic differs from conventional additive logic where factors on average contribute to an outcome and can compensate for each other. NCA complements conventional methods such as multiple regression and structural equation modeling. Applying NCA can provide new theoretical and practical insights by identifying the level of a factor that must be put and kept in place for having the outcome. A necessary condition that is not in place guarantees failure of the outcome and makes changes of other contributing factors ineffective.
NCA’s data analysis allows for a (multiple) bivariate analysis. NCA puts a ceiling line on the data in an XY-scatter plot. This line separates the space with cases from the space without cases. An empty space in the upper left corner of the scatter plot indicates that the presence of X is necessary for the presence of Y. The larger the empty space relative to the total space, the more X constrains Y, and the more Y is constrained by X, hence the larger the necessity effect size. A point on the ceiling line represents the level Xc of X that is necessary, but not sufficient, for level Yc of Y.
NCA is applicable to any discipline. It has already been applied in various business and management fields including strategy, organizational behavior, human research management, operations, finance, innovation, and entrepreneurship. More information about the method and its free R software package can be found on the NCA website.