Accurately predicting precise outcomes in business-to-business marketing
dc.contributor.author | Woodside, Arch | |
dc.date.accessioned | 2019-02-19T04:18:24Z | |
dc.date.available | 2019-02-19T04:18:24Z | |
dc.date.created | 2019-02-19T03:58:36Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Woodside, A. 2018. Accurately predicting precise outcomes in business-to-business marketing. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/74909 | |
dc.identifier.doi | 10.1108/S1069-096420180000025006 | |
dc.description.abstract |
Copyright © 2018 by Emerald Publishing Limited. This chapter identifies research advances in theory and analytics that contribute successfully to the primary need to be filled to achieve scientific legitimacy: Configurations that include accurate explanation, description, and prediction-prediction here refers to predicting future outcomes and outcomes of cases in samples separate from the samples of cases used to construct models. The MAJOR PARADOX: Can the researcher construct models that achieve accurate prediction of outcomes for individual cases that also are generalizable across all the cases in the sample? This chapter presents a way forward for solving the major paradox. The solution here includes philosophical theoretical, and operational shifts away from variable-based modeling and null hypothesis statistical testing (NHST) to case-based modeling and somewhat precise outcome testing (SPOT). These shifts are now occurring in the scholarly business-to-business literature. | |
dc.title | Accurately predicting precise outcomes in business-to-business marketing | |
dc.type | Book | |
dcterms.source.volume | 25 | |
dcterms.source.startPage | 63 | |
dcterms.source.endPage | 84 | |
curtin.department | School of Marketing | |
curtin.accessStatus | Fulltext not available |
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