The present study applies asymmetric analysis and models complex antecedent conditions to identify shoppers with high purchase intentions to sustainable fashion products’ (SFPs) and high eWOM intention. The fuzzy-set qualitative comparative analysis (fsQCA) method was used to assess the cause-and effect process. The examination was based on information process, and decision making of consumers in two countries (China and Korea) was found to vary by nationality. Specifically, consumers in the two countries provided different responses on sustainable fashion change configuration, suggesting differences in the characteristics of sustainable and non-sustainable fashion consumers and sustainable fashion intentions. The results show that various casual recipes on sustainable fashion change the configuration and sustainable fashion intention on corners 1 and 4. Both Chinese and Korean consumers do not have several unique demographic and fashion expenditure configurations that characterize consumers with high intention to buy and eWOM intention favorable toward sustainable fashion. In the Chinese consumers’ data, computing with words (CWW) showed that young•married•low-income•low-education•low-fashionexpenditure cases (consumers) were lower on negation purchase and eWOM intentions (i.e., an accurate screening configuration identifying consumers high io non-sustainable fashion intentions). The results also help identify consumer characteristics of sustainable fashion consumers and non-sustainable fashion consumers. Specifically, the results of the fsQCA suggest dissimilar confirmation to achieve purchase intention and eWOM intention of sustainable fashion and provide meaningful academic and managerial implications. The results of the fuzzy-set qualitative comparative analysis also support and clarify the role of the theory of information process and the theory of reasoned action towards sustainable fashion.

The major paradox in research in marketing: Can the researcher construct models that capture firm heterogeneities and achieve accurate prediction of outcomes for individual cases that also are generalizable across all the cases in the sample? This study presents a way forward for solving the major paradox. The study identifies research advances in theory and analytics that contribute successfully to the primary need to fill to achieve scientific legitimacy: Configurations that include accurate description, explanation, and prediction (i.e., predicting outcomes accurately of cases in samples separate from the samples of cases used to construct models having high fit validity.) 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). The study here provides examples of research contributing to knowledge and theory that advance prediction and control in business-to-business contexts. Shifting beyond linear model construction and symmetric tests (i.e., multiple regression analysis (MRA) and structural equation modeling (SEM)) and embracing complexity theory and asymmetric tests (i.e., constructing and testing algorithms by “computing with words,” Zadeh, (1996, 2010)) includes taking necessary steps away from examining “net effects” of variables to useful screening modeling of case configurations. Researchers embracing this shift in marketing benefit from recognizing that the current dominant logic of performing null hypothesis testing (NHST via MRA and SEM) is “corrupt research” (Hubbard, 2015) and from recognizing that predicting by algorithms via somewhat precise outcome testing (SPOT) advances business-to-business research toward achieving scientific legitimacy.