Predictive Modeling in Logistics and Supply Chain Management Research Using Partial Least Squares Structural Equation Modeling
Call for papers for: International Journal of Physical Distribution & Logistics Management
Submissions open: 1st Jan 2022
Submissions deadline: 31st March 2022
Aims and Scope
According to Hofstader (1951: 339), “[p]rediction and explanation are the two main functions of scientific knowledge”. Despite seeming to represent competing goals, prediction and explanation do not actually do so. Explanatory modeling involves using statistical models to test causal explanation, 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: 291). Explanations provide a cognitive path to predictions and help us grapple conceptually with a complicated world, while predictions serve to test and refine explanations (Douglas, 2009). Nevertheless, explanation has played a dominant role in social science research, answering ‘why’ questions by explaining causal mechanisms. However, by merely focusing on explanation without considering prediction, we have compromised our ability to understand explanation (Douglas, 2009).
In the logistics and supply chain management (LSCM) field, explanatory modeling is often used to test theories, with technology adoption being a good example of such models. In the 21st century, the world of LSCM uses unprecedented disruptive innovations that technologies, such as artificial intelligence, blockchain, robotics, and robotic process automation, provide. These technologies pave the way for SCM 4.0 (Hofmann et al., 2019), operator 4.0 (Romero et al., 2020), smart manufacturing (Kusiak, 2018), and logistics 4.0 developments (Winkelhaus and Grosse, 2020) toward the industry 4.0 era. Using causal hypotheses to explain technology adoption is therefore rather normal. However, a focus on explanation can lead to the proliferation of theories that explain the same phenomenon. A predictive model helps limit the explanatory options that provide a more reliable basis for decision making (Douglas, 2009).
Scientific inquiry is used to “facilitate prediction, intervention, control, or other forms of actions” (Longino, 2002: 124). Explanatory modeling is not good at predicting model parameters for new observations (Hair & Sarstedt, 2021). Predictive power, on the other hand, is a model’s ability to generate reasonable predictions for new observations (Shmueli & Koppius, 2011). Predictive modeling can test the predictive power of specific explanatory variables and their practical relevance. Moreover, it can be used to uncover potential new causal mechanisms and new hypotheses, new measures, underlying complex patterns, and relationships to assess the distance between theory and practice, to compare competitive theories, and to quantify the level of predictability or that of the measurable phenomenon (Shmueli, 2010).
The LSCM field can be complemented by emphasizing prediction more. LSCM researchers often frame their managerial recommendations as prescriptive statements—which follow inherent prediction logic. However, LCSM managers could affect their employees’ job performance and work engagement positively if they were to invest in technology that their employees not only find easy to use, but actually useful. The latter conditional statement could foreshadow a specific result if a certain activity is implemented (i.e., prescriptive statements). Making such statements, however, requires verifying their adequacy by conducting an additional assessment of the models’ prediction (Shmueli & Koppius, 2011). Predictive models can help verify and ensure a model’s predictive capability, which is the sine qua non condition for its relevance in respect of decision making and providing recommendations for business practice (Chin et al. 2020; Shmueli et al., 2016). LSCM researchers’ primary objective should go beyond simply assessing whether the model coefficients are significant and in the hypothesized direction in order to test whether a theory can accurately predict an outcome of interest (e.g., company performance, risk resilience, or technology effectiveness). Instead of testing hypothesized relationships embedded in a nomological network (Stank et al. 2019; Yawar and Seuring, 2017), given the existing data input, we should develop skills to predict new data outcomes.
Research on LSCM is increasingly shifting from universalistic to more multifaceted models (e.g., Bubicz et al., 2019; Yawar and Seuring, 2017). At best, the use of general theory and abstract constructs provides abstract explanations (Garver, 2019). We have perhaps overemphasized theories that offer elegant explanation based on suboptimal models (Schorsch et al., 2017). We need better explanatory and predictive models to generate theoretical insights and practical implications. We have, for instance realized that actors and decision makers in supply chains behave differently than theory predicts (Schorsch et al., 2017). Complex LSCM phenomena are better understood by using predictive modeling to test our models’ predictive power. For example, we need to better understand that technology developments impact the way humans work significantly. Using both explanatory and predictive modeling, we can test and refine a theoretical explanation of human interactions with technology.
The emergence of increasingly complex models underscores the critical importance of using advanced multivariate analysis techniques, such as structural equation modeling (SEM) (see Kaufmann and Gaeckler, 2015). SEM has become a quasi-standard tool for analyzing complex interrelationships between observed and latent variables. Covariance-based SEM (CB-SEM) is arguably the most important method for testing and confirming theoretically established models and their hypothesized relationships. CB-SEM is an explanatory modeling technique that relies on factor-based SEM. CM-SEM uses maximum likelihood (ML) to estimate model parameters with the aim of minimizing the discrepancy between the estimated and sample covariance matrices (Hair et al., 2022). This approach to SEM strongly emphasizes assessing a model’s goodness-of-fit criteria (Bagozzi & Yi, 2012), which quantify the difference between the empirical covariance matrix and the model-implied covariance matrix. The result indicates how well the hypothesized model fits the indicator data at hand, as an indication of how well the presumed causal mechanisms explain the hypothesized relationships. In terms of their significance, size, and direction, model parameters (e.g., beta coefficients) are used to test the hypotheses, while explanatory models’ explanatory power is measured in terms of F-statistics and R-square.
Partial least squares SEM (PLS-SEM) is another approach (Rigdon, Sarstedt, and Ringle 2017). PLS-SEM is a causal-predictive approach to SEM (Hair et al., 2019), aiming (i) to predict key target constructs and to identify key driver constructs, as well as (ii) to explore (and to extend) an existing structural theory. PLS-SEM is a composite-based SEM that maximizes the explained variance of the endogenous constructs or indicators (Hair et al., 2022). Consequently, the “unobservable variables are estimated as exact linear combinations of their empirical indicators” (Fornell and Bookstein, 1982, p. 441), such that the resulting composites capture most of the variance of the exogenous constructs’ indicators, which is, in turn, useful for predicting the endogenous constructs’ indicators (e.g., Hair and Sarstedt, 2021).
This special issue focuses on PLS-SEM, not because CB-SEM is less useful, but mainly because PLS-SEM’s potentials have not been fully exploited (Kaufmann and Gaeckler, 2015). It is important to be aware of the heated debates on its myths, which partly comprise unilateral arguments with deliberately fabricated evidence against the method (Reinartz et al., 2009; Rönkkö and Evermannm, 2013; Henseler et al., 2014; Rönkkö et al. 2016), using appropriate, but also rather weak, arguments to justify its use (Rigdon, 2016; Rigdon et al., 2017), and offering remedies for its weaknesses (Henseler et al., 2014; McIntosh et al., 2014; Sarstedt et al., 2016). Like any statistical method, PLS-SEM has its advantage and disadvantage, it is not a silver bullet per se (Henseler et al. 2014; Marcoulides and Chin, 2013; Petter, 2018). Researchers should therefore understand the correct reasons for using the PLS-SEM method (Rigdon et al. 2017). Furthermore, they should only use PLS-SEM for reasons that fit their study objectives (Hair et al., 2019a, b; Hair et al., 2022; Richter, Cepeda-Carrión, Roldán, and Ringle, 2016). In addition, researchers must take care to present, report, and interpret the analysis results appropriately, which will ensure the rigor of their research (Hair, Ringle, and Sarstedt, 2013; Hair et al., 2019a; Hair et al., 2020; Sarstedt, Ringle, Cheah, Ting, Moisescu, and Radomir, 2020). Finally, PLS-SEM is still developing numerous new analysis options and evaluation/report criteria (e.g., confirmatory composite analysis, PLSpredict, model selection criteria, the cross-validated predictive ability test, etc.) (see Hair et al., 2018; Hair, Howard, and Nitzl, 2020; Chin et al., 2020; and Liengaard, et al., 2021).
In a nutshell, the aim of this special issue of International Journal of Physical Distribution & Logistics Management (IJPDLM) is to publish LSCM research that improves explanation and prediction for a wider audience by using PLS as a prediction-oriented approach to SEM. We are looking for high-quality and innovative papers that use PLS-SEM and related methods (including comparisons with CB-SEM) to address the interplay between explanation and prediction in order to advance our understanding and knowledge of the LSCM field. In addition, we also call for technical papers that introduce methodological advances in PLS-SEM and related multi-method approaches (e.g., confirmatory tetrad analysis, importance-performance map analysis, necessary condition analysis, fuzzy set qualitative comparative analysis, predictive model comparison using CVPAT, and segmentation) that strongly emphasize their explanatory and predictive aspects. The relevant papers should illustrate how the original PLS-SEM’s proposed advances are relevant in practice to predict behavior, decision-making processes, and business performance in the field of LSCM phenomena.
The special issue encourages authors with outstanding papers presented at the 3rd International Symposium on Applied Structural Equation Modeling and Methodological Matters, as well as the Hamburg International Conference of Logistics (2021), to submit papers that fall within the scope and aim of IJPDLM. An example of a special issue, comprising 17 articles, published on the basis of the above conference was edited by Cepeda-Carrion et al. (2016) in the Journal of Business Research. Authors are encouraged to extend their studies related to LSCM by using predictive models and PLS-SEM (e.g., Felipe et al., 2016; Gelhard and von Delft, 2016; Vos et al., 2016). However, the guest editors also welcome high-quality submissions that were not submitted to or presented at these conferences.
Researchers are invited to consider and submit high-quality, original work that has not been published, nor been considered for publication by any other journals.
Submissions must not exceed 10,000 words in length. This includes all the text, for example, the structured abstract, references, all text in tables, in figures, and appendices. Please allow 280 words for each figure or table. Longer manuscripts will be returned to the authors without being reviewed. For further information and formatting requirements, please visit the International Journal of Physical Distribution & Logistics Management website at; https://www.emeraldgrouppublishing.com/journal/ijpdlm?_ga=2.163463227.604404725.1617528938-156123007.1609748740#author-guidelines
Manuscripts should be submitted to the journal through the online submission system https://mc.manuscriptcentral.com/ijpdlm. The guest editors/editorial desk will review all submissions to initially determine their suitability and relevance for publication in this special issue. Papers that lack originality and clarity, or fall outside the special issue’s scope will not be submitted for blind review, and the authors will be promptly informed. The final acceptance of manuscripts depends on their quality and review reports.
- Paper submission deadline: March 31, 2022
- Initial review report: May 31, 2022
- Revised manuscript due: July 1, 2022
- Second round of review report: August 15, 2022
- Final acceptance notification: September 30, 2022
Jun-Hwa Cheah (Jacky), Universiti Putra Malaysia, Malaysia
Wolfgang Kersten, Hamburg University of Technology, Germany
Christian M. Ringle, Hamburg University of Technology, Germany, and
University of Waikato, Hamilton, New Zealand
Carl Marcus Wallenburg, WHU – Otto Beisheim School of Management, Germany
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