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

Abstract

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(Hair et al., , 2022)).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 (Douglas, 2009).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 (Douglas, 2009).In the logistics and supply chain management (LSCM) field, the explanatory modeling paradigm is often used to test theories.Currently, for instance, many LSCM researchers are interested in understanding LSCM's effects and benefits not only in their research field, but also in those of unprecedented technologies and innovations, such as artificial intelligence, blockchain, metaverse, robotics and robotic process automation.These technologies pave the way for supply chain management (SCM) 4.0 (Hofmann et al., 2019), supply chain (SC) analytics (Lodemann et al., 2022), operator 4.0 (Romero et al., 2020), smart manufacturing (Kusiak, 2018), metaverse (Queiroz et al., 2023) and logistics 4.0 developments (Winkelhaus and Grosse, 2020) toward the 4.0 revolution industry era.Simultaneously, however, SCs need to become more sustainable (Brockhaus et al., 2016;Sarkis, 2020) and resilient to disruption (Wieland and Durach, 2021;Novak et al., 2021).This also applies to technological innovations as well as to structural changes and organizational measures.Consequently, establishing and empirically testing causal hypotheses to explain such far-reaching changes are often common research procedures (e.g.Mathauer and Hofmann, 2019;Richter et al., 2020).However, strongly focusing on explanations could lead to an increasing number of theories aimed at explaining a certain phenomenon of interest, while a predictive model helps restrict the possible explanations and provides a reliable basis for decision making (Douglas, 2009).Scientific inquiry is specifically used to "facilitate prediction, intervention, control, or other forms of actions" (Longino, 2002, p. 124).Explanatory modeling does not predict model parameters for new observations (Hair and Sarstedt, 2019), but predictive modeling helps assess specific explanatory variables' predictive power and, therefore, their practical relevance.More specifically, predictive power depicts a model's ability to generate adequate predictions for new observations (Shmueli and Koppius, 2011).Further, predictive modeling allows

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