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    Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities

    227357_227357.pdf (1.048Mb)
    Access Status
    Open access
    Authors
    Woodside, Arch
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Woodside, A. 2014. Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities. Journal of Business Research. 67 (12): pp. 2495-2503.
    Source Title
    Journal of Business Research
    DOI
    10.1016/j.jbusres.2014.07.006
    ISSN
    0148-2963
    URI
    http://hdl.handle.net/20.500.11937/35555
    Collection
    • Curtin Research Publications
    Abstract

    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.

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