Show simple item record

dc.contributor.authorWoodside, Arch
dc.identifier.citationWoodside, A. 2014. Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities. Journal of Business Research. 67 (12): pp. 2495-2503.

This essay describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2 to 7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C)-. and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities-configural analysis-is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.

dc.subjectContrarian case
dc.titleEmbrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities
dc.typeJournal Article
dcterms.source.titleJournal of Business Research
curtin.accessStatusOpen access

Files in this item


This item appears in the following Collection(s)

Show simple item record