Guest editorial: Predictive modeling in logistics and supply chain management research using partial least squares structural equation modeling

Predictive modeling in logistics and supply chain management research using partial least squares structural equation modeling There are many types of empirical research objectives that researchers can pursue in business, economic and social science research. These can be divided into five categories: confirmatory, explanatory, exploratory, descriptive and predictive research (Hair et al., 2019 In this respect, Hofstadter (1951, p. 339) claims, "explanation and prediction are the two commonly used functions of scientific knowledge." This is still true today. Although prediction and explanation appear to be opposing goals, they do not contradict each other. Explanatory modeling uses statistical models to test causal relationships, while predictive modeling is defined as "the process of applying a statistical model or [a] data mining algorithm to data for the purpose of predicting new or future observations" (Shmueli, 2010, p. 291). Explanations could provide a cognitive pathway to predictions by helping us conceptualize complex phenomena; predictions, on the other hand, serve to test and refine explanations Nevertheless, explanation plays a dominant role in social science research by answering the why question that explains the causal mechanisms. If researchers only focus on the explanation without considering the prediction, they compromise their ability to understand the explanation