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.
Woodside, A.G. (2018), "Accurately Predicting Precise Outcomes in Business-to-Business Marketing", Improving the Marriage of Modeling and Theory for Accurate Forecasts of Outcomes (Advances in Business Marketing and Purchasing, Vol. 25), Emerald Publishing Limited, pp. 63-84. https://doi.org/10.1108/S1069-096420180000025006Download as .RIS
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