Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities
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...
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| Format: | Journal Article |
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Elsevier
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/35555 |
| _version_ | 1848754528233455616 |
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| author | Woodside, Arch |
| author_facet | Woodside, Arch |
| author_sort | Woodside, Arch |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:41:50Z |
| format | Journal Article |
| id | curtin-20.500.11937-35555 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:41:50Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-355552017-09-13T15:57:55Z Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities Woodside, Arch Model Antecedent Contrarian case FsQCA Configuration Necessary 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/35555 10.1016/j.jbusres.2014.07.006 Elsevier fulltext |
| spellingShingle | Model Antecedent Contrarian case FsQCA Configuration Necessary Woodside, Arch Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities |
| title | Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities |
| title_full | Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities |
| title_fullStr | Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities |
| title_full_unstemmed | Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities |
| title_short | Embrace•Perform•Model: Complexity Theory, Contrarian Case Analysis, and Multiple Realities |
| title_sort | embrace•perform•model: complexity theory, contrarian case analysis, and multiple realities |
| topic | Model Antecedent Contrarian case FsQCA Configuration Necessary |
| url | http://hdl.handle.net/20.500.11937/35555 |