پیش بینی نتایج در بازاریابی تجارت به تجارت
ABSTRACT
1- INTRODUCTION: ACHIEVING SCIENTIFIC LEGITIMACY
2- PREDICTING WHAT DIRECTIONS OR OUTCOMES?
3- PREDICTING PRECISE OUTCOMES IN THE B-TO-B LITERATURE
4- EMBRACING COMPLEXITY THEORY AS THE FOUNDATIONAL PHILOSOPHY IN B-TO-B RESEARCH
5- CONCLUDING REMARKS
REFERENCES
ABSTRACT
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.
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).