INTRODUCTION: ACHIEVING SCIENTIFIC LEGITIMACY

LaPlaca and colleagues (Hadjikhani & LaPlaca, 2013; LaPlaca, 1997; LaPlaca & da Silva, 2016) described in-depth the first paradigm shift in business-to-business (B-to-B) research from description and explanation of business exchanges based on transactions to description and explanation of business exchanges based on relationships. Equally important, they identify what is still necessary to accomplish for B-to-B research to achieve scientific legitimacy, “B2B relationships as a subject of scientific enquiry will need to seriously engage into what can be termed a true paradigm shift, one that advances discovery in this area from sheer descriptive analysis and reporting to the development of explanatory schemata and theoretical frameworks of a kind that allow for more accurate prediction of underlying B2B phenomena” (LaPlaca & da Silva, 2016, p. 232). LaPlaca and colleagues provide foundation insights into the steps necessary to be taken to achieve scientific legitimacy, including embracing prediction and control as necessary objectives in B-to-B research
research focusing on description and explanation is necessary but insufficient for advancing science in the B-to-B discipline. “In conducting scientific investigations, researchers, particularly scientists studying physical phenomena, progress through a hierarchy of types of research: descriptive, explanatory, predictive, and control (LaPlaca, 2013). The ultimate goal of science is to control events where possible … Improved understanding and predictive capabilities will reduce marketing errors and improve overall marketing effectiveness and efficiency. In this way, B-to-B marketing research will truly make a contribution to society” (LaPlaca & da Silva, 2016, p. 232). The following discussion focuses on how to accomplish the true paradigm shift that LaPlaca and colleagues identify. The study here provides examples of research contributing to knowledge and theory that advance prediction and control in B-to-B contexts. The study indicates that 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) are necessary steps to be taken to accomplish the true paradigm shift. Researchers in B-to-B research benefit from recognizing that the current dominant logic of performing null hypothesis testing (NHST via MRA and SEM) is “corrupt research” (Hubbard, 2016) practice and from recognizing that predicting by algorithms via somewhat precise outcome testing (SPOT) advances B-to-B research toward achieving scientific legitimacy. Following this introduction, the second section answers the question, “Predicting what
directions or outcomes?” The third section provides examples of predicting precise outcomes in the B-to-B research literature.