Call for Papers - Advanced Partial Least Squares Path Modeling (PLS-PM) Applications in Social Sciences

Guest Editors
Jun-Hwa Cheah, Universiti Putra Malaysia,
Jan-Michael Becker, University of Cologne,
Enrico Ciavolino, University of Salento,
Biagio Simonetti, University of Sannio

Motivation and aims of the special issue
Partial Least Squares Path Modeling (PLS-PM) has become a well-established multivariate analysis methods toolbox in social sciences (Hair, Black, Babin, & Anderson 2018). Henseler (2018) has highlighted that the use of PLS-PM’s able to handle highly complex path models and its causal-predictive nature, which allows bridging the apparent dichotomy between explanation and prediction. While its usage spans across multiple fields outside the social sciences, the mainstay of PLS-SEM is business research. It is not surprising that some of the most cited articles in the Quality and Quantity use the PLS-PM method (see Cheah, Ting, Ramayah, Memon, Cham, & Ciavolino, 2019; Ciavolino, Salvatore, Mossi, & Lagetto, 2018; Schuberth, Henseler, & Dijkstra, 2018).

Recent research has brought forward numerous methodological extensions that allow social science researcher to generate nuanced assessment of results. For example, with the support of PLS-PM, social sciences today able to conduct advanced assessment, such as 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 practical recommendations.

Call for Papers
The aim of this special issue of Quality & Quantity 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 social sciences. In addition, methodological advances of the PLS-SEM method will also be considered. Potential topics include, but are not limited to:

Applications and advancements of the original PLS-SEM algorithmDifferences in model development from explanatory vs. predictive perspectives,Analysis of complex model relationships involving nonlinear effects, multiple mediation, and/or moderated mediation, higher-order models,New metrics for goodness-of-fit testing and predictive power assessment,Using PLS-SEM in experimental research (e.g., discrete choice modelling data),Model comparisonsEndogeneity 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, and other 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 ( to be held March 17-19, 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 social sciences.

The manuscript must fully comply with the instructions for authors: manuscript guidelines. 

Authors must use the official Quality and Quantity Submission Portal, and select ‘Advanced PLS-SEM’ special issue for their submission. The submission portal will open on June 1, 2020, and will close on July 31, 2020.

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 Quality and Quantity (or will be rejected otherwise). There is no guarantee of publication.

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

•           Submission due date: July 31, 2020
•           First round reviews: September 30, 2020
•           Revisions due: November 15, 2020
•           Second round reviews: January 31, 2021
•           Revisions due: March 14, 2021
•           Final editorial decision: April 18, 2021

Cheah, J. H., Ting, H., Ramayah, T., Memon, M. A., Cham, T. H., & Ciavolino, E. (2019). A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research. Quality & Quantity, 53(3), 1421-1458. Ciavolino, E., Salvatore, S., Mossi, P., & Lagetto, G. (2018). High-order PLS path model for multi-group analysis: the prosumership service quality model. Quality & Quantity, Forthcoming.Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2018). Multivariate Data Analysis. Mason, OH: Cengage.Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage.Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage.Henseler, J. (2018). Partial least squares path modeling: Quo vadis?. Quality & Quantity52(1), 1-8.Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L (2019). Structural Model Robustness Checks in PLS-SEM. Tourism Economics, forthcoming.Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Partial least squares path modeling using ordinal categorical indicators. Quality & Quantity52(1), 9-35.Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray, S (2019). Prediction-oriented Model Selection in Partial Least Squares Path Modeling. Decision Sciences, in press.Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564.