Composite-based partial least squares structural equation modeling (PLS-SEM) has become a well-established element in researchers’ multivariate analysis methods toolbox (Hair, Black, Babin, & Anderson 2018). Particularly PLS-SEM’s ability to handle highly complex path models and its causal-predictive nature, which allows bridging the apparent dichotomy between explanation and prediction, have contributed to its massive dissemination. While its usage spans across multiple fields outside the social sciences, the mainstay of PLS-SEM is business research. Some of the most popular models in the fields – including customer satisfaction and loyalty models (e.g., Ahrholdt, Gudergan, & Ringle 2019), corporate reputation models (e.g., Hult, Hair, Proksch, Sarstedt, Pinkwart, & Ringle 2018), and technology acceptance models (e.g., Schubring, Lorscheid, Meyer, & Ringle 2016) – are routinely estimated using PLS-SEM. It is not surprising that some of the most cited articles in the Journal of Business Research (JBR) use the PLS-SEM method (e.g., Coltman, Devinney, Midgley, & Venaik 2008; Camisón & Villar-López 2014).



Recent research has brought forward numerous methodological extensions that allow for a more nuanced assessment of results. These extensions include, for example, latent class segmentation, model comparisons, endogeneity assessment, and predictive model evaluation (Hair, Hult, Ringle, & Sarstedt 2017; Hair, Sarstedt, Ringle, & Gudergan 2018). Especially the prediction-oriented PLS-SEM analyses (Shmueli, Ray, Velasquez Estrada, & Chatla 2016; Sharma, Shmueli, Sarstedt, Danks, & Ray 2019) and methods to assess the result’s robustness (Sarstedt, Ringle, Cheah, Ting, Moisescu, & Radomir 2019) are particularly important to substantiate findings, conclusions, and managerial recommendations.



The aim of this special issue of JBR is to introduce advanced PLS-SEM methods to a wider audience. The special issue embraces the applications of advanced PLS-SEM methods to generate new insights and shed new light on existing models and theories. In addition, methodological advances of the PLS-SEM method will also be considered. Potential topics include, but are not limited to:



Differences in model development from explanatory vs. predictive perspectives,Explanatory versus predictive model evaluation,New metrics for goodness-of-fit testing and predictive power assessment,Using PLS-SEM in experimental research and on experimental data (e.g., discrete choice modelling data),Endogeneity in PLS-SEM,Common method variance in PLS-SEM,Using PLS-SEM with archival (secondary) data,Addressing observed (multi-groups analysis and moderation) and unobserved heterogeneity (segmentation) in PLS-SEM,Using PLS-SEM on panel or longitudinal data,Combining Bayesian modeling and PLS-SEM, andOther advanced developments of PLS-SEM and their application.

Submission and review process



Manuscripts should not have been previously published or be under consideration by other journals. The special issue is tied to the 2020 International Conference on Partial Least Squares Structural Equation Modeling (www.pls2020.org) to be held October 27-30, 2020 in Beijing, China. Outstanding papers presented at this conference will be invited for submission. However, the guest editors also welcome submissions of high-quality papers that have not been submitted to or presented at the conference. Authors who submit papers that have not been presented at the 2020 International Conference on Partial Least Squares Structural Equation Modeling must explicitly state in their cover letter what is unique and valuable about the paper within the context of presenting an advanced PLS-SEM application in business research.



The manuscript must fully comply with the JBR author guidelines.



Authors must use the official JBR submission portal, and select ‘Advanced PLS-SEM’ special issue for their submission. The submission portal will open on November 15, 2022 and will close on January 18, 2023.



All papers will be screened by at least two guest editors (and desk rejected if not deemed suitable) before being sent to at least two referees. Papers will undergo a maximum of two rounds of revision to meet the scope and high standards of JBR (or will be rejected otherwise). There is no guarantee of publication.



The publication of this special issue is anticipated at the end of 2021.



For any queries regarding submission, please contact the special issue guest editors via email.



Special issue guest editors



Marko Sarstedt, Otto-von-Guericke University, Germany, and Monash University of Malaysia, Malaysia, marko.sarstedt@ovgu.de



Christian M. Ringle, Hamburg University of Technology (TUHH), Germany, and University of Waikato, New Zealand, c.ringle@tuhh.de



Lacramioara Radomir, Babe?-Bolyai University, Romania, lacramioara.radomir@econ.ubbcluj.ro



Ovidiu I. Moisescu, Babe?-Bolyai University, Romania, ovidiu.moisescu@econ.ubbcluj.ro



Siegfried P. Gudergan, University of Waikato, New Zealand, siggi.gudergan@waikato.ac.nz



References



Ahrholdt, D. C., Gudergan, S. P., & Ringle, C. M. Enhancing Loyalty: When Improving Consumer Satisfaction and Delight Matters. Journal of Business Research 2019; 94 (1): 18-27.



Camisón, C. & Villar-López, A. Organizational Innovation as an Enabler of Technological Innovation Capabilities and Firm Performance. Journal of Business Research 2014; 67 (1): 2891-2902.



Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. Formative Versus Reflective Measurement Models: Two Applications of Formative Measurement. Journal of Business Research 2008; 61 (12): 1250-1262.



Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. Multivariate Data Analysis. Mason, OH: Cengage, 2018.



Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage, 2017.



Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage, 2018.



Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling. Journal of International Marketing 2018; 26 (3): 1-21.



Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. Structural Model Robustness Checks in PLS-SEM. Tourism Economics 2019; forthcoming.



Schubring, S., Lorscheid, I., Meyer, M., & Ringle, C. M. The PLS Agent: Predictive Modeling with PLS-SEM and Agent-based Simulation. Journal of Business Research 2016; 69 (10): 4604-4612.



Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray, S. Prediction-oriented Model Selection in Partial Least Squares Path Modeling. Decision Sciences 2019; in press.



Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. The Elephant in the Room: Evaluating the Predictive Performance of PLS Models. Journal of Business Research 2016; 69 (10): 4552-4564.