Embracing the complexity turn in management research for modeling multiple realities
© Springer International Publishing AG 2017. All rights reserved. Chapter 2 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 positive...
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| Format: | Book Chapter |
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2017
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| Online Access: | http://hdl.handle.net/20.500.11937/62978 |
| _version_ | 1848760961154940928 |
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| author | Woodside, Arch |
| author_facet | Woodside, Arch |
| author_sort | Woodside, Arch |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © Springer International Publishing AG 2017. All rights reserved. Chapter 2 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., 2-7 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 | 2025-11-14T10:24:05Z |
| format | Book Chapter |
| id | curtin-20.500.11937-62978 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:24:05Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-629782018-02-06T06:23:51Z Embracing the complexity turn in management research for modeling multiple realities Woodside, Arch © Springer International Publishing AG 2017. All rights reserved. Chapter 2 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., 2-7 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. 2017 Book Chapter http://hdl.handle.net/20.500.11937/62978 10.1007/978-3-319-47028-3_1 restricted |
| spellingShingle | Woodside, Arch Embracing the complexity turn in management research for modeling multiple realities |
| title | Embracing the complexity turn in management research for modeling multiple realities |
| title_full | Embracing the complexity turn in management research for modeling multiple realities |
| title_fullStr | Embracing the complexity turn in management research for modeling multiple realities |
| title_full_unstemmed | Embracing the complexity turn in management research for modeling multiple realities |
| title_short | Embracing the complexity turn in management research for modeling multiple realities |
| title_sort | embracing the complexity turn in management research for modeling multiple realities |
| url | http://hdl.handle.net/20.500.11937/62978 |