CONCLUDING REMARKS
Fig. 4 is a visual summary of core features in the current dominant logic in B-to-B research and core features of a true new paradigm now in the introduction stage in the discipline. The features appear in two Venn diagrams in Fig. 4 to represent the configurational nature of research paradigms rather than a house-of-cards, research paradigms include self-reinforcing joined-at-the-hip forces. Replacing one bad feature alone is insufficient. All features need replacing or dramatically improving by a truly new research paradigm. The evolutionary rise in the current dominant variable-based mostly descriptionand explanation-focused logic in B-to-B research occurred in the 1950s and continued to the end of the 20th century. The revolutionary introduction of a true, new, case-based paradigm focusing mostly on description, explanation, and prescription is occurring in the second decade of the 21st century. Growth is expanding rapidly now (20152019) in the number of scholarly articles featuring the true new paradigm (Roig-Tierno, Gonzalez-Cruz, & Llopis-Martinez, 2017).
Twenty paradigm shift-catalysts appear in the center of Fig. 4. These shiftcatalysts are essays and mostly non-NHST SPOT-empirical studies that include features and full-blown expositions of a true new research paradigm. The 20 catalysts include Hubbard’s (2016) thorough documentation of the corrupt practices of NHST the foundational analytical stance of the current dominant logic. Because NHST is a bad science practice, the editor of one prestigious scholarly journal (Basic and Applied Social Psychology) announced that authors of all future articles accepted for publication would need to remove reports of statistical significance tests before their articles were published (Trafimow & Marks, 2015). NHST is more than a tool for data analysis; the use of NHST suggests embracing a theoretical stance. Unfortunately, the current dominant logic and use of correlations, F-tests, MRA, and SEM nurture the perspective that NHST is the only scientific testing procedure worthy of using. Reading Hubbard (2016) is very helpful for overcoming such a sad and wrong conclusion. Woodside (2017) expands on Hubbard’s (2016) call to use “statistical sameness” outcome testing by presenting several studies that do just that.
