Model Selection in Marketing Research

Deadline for submissions 1 June 2026

Summary

This special issue of JMM calls for papers which explore decisions in the model selection process in terms of conceptual lenses, metrics, and best practices of model selection.

Call for Papers

Guest Editors

  • Marko Sarstedt, Ludwig-Maximilians University Munich, Germany and Babeș-Bolyai University, Romania;
  • Francesca Magno, University of Bergamo, Italy;
  • Vasilica-Maria Margalina, CESINE University Center, Spain;
  • Fabio Cassia, University of Verona, Italy;
  • Christian M. Ringle, Hamburg University of Technology, Germany and James Cook University, Australia.

Aims & Scope of the Special Issue

Models simplify reality to reveal key relationships. While they can’t capture all its complexity, their strength lies in clarity and focus. While highlighting meaningful relationships, no single model fully explains a phenomenon. Exploring theoretically plausible alternative models for explaining the phenomenon under study is therefore a crucial step in advancing scientific knowledge in that it promotes transparency and challenges the overreliance on any one framework.

Alternative models typically emerge when considering theories in new contexts with unique variables and effects, or when researchers build conceptual bridges across related streams of inquiry to provide a holistic understanding of the phenomenon. Given a set of alternative models, researchers then try to identify the model that best approximates the data generation process underlying the phenomenon under study. The ability to explain and confirm theories or predict other data is often considered as an indicator of model’s usefulness or quality. Hence, model selection may focus on explanation and confirmation of theoretical models, on their predictive power, or on an integrated combination of both. This distinction also implies fundamental choices about the nature and purpose of the knowledge produced, which are crucial for appropriate model identification, selection, and evaluation. Thus, different methods and criteria have been proposed for model selection in literature. Model selection is a complex process that involves numerous decisions made by researchers, informed by theory, methods, data, and results. This complexity raises some remaining open questions regarding the selection methods that must be used in each research context.

This special issue of Journal of Marketing Management (JMM) focuses on decisions in the model selection process in terms of conceptual lenses, metrics, and best practices of model selection. We consider studies that address key challenges in each step of the model selection process, including model identification, selection, and evaluation. Studies may also explore the philosophical dimensions of model selection, particularly in the context of open science. The special issue is methods-agnostic, and we welcome submissions related, but not limited to structural equation modeling, latent class analysis, and regression-based methods such as necessary condition analysis, as well hybrid or integrated approaches.

Potential Topics of Interest

  • Explanatory and predictive model selection process and criteria
  • Integrated approaches combining explanatory and predictive goals
  • Multimodel inference (e.g., model weights, ensemble models)
  • Segmentation and model-based clustering
  • Use and interpretation of fit and modification indices
  • Search algorithms for model optimization
  • Use of control variables and boundary conditions
  • Integration of quantitative selection criteria with theoretical judgment
  • Conceptual and philosophical concerns related to model selection and multimodel inference

In alignment with contemporary developments in research transparency, we strongly encourage methodological contributions (e.g., with empirical illustrations) that emphasize the principles of open science. Rather than solely focusing on identifying a single “true” model, this issue invites submissions that highlight transparency throughout the model selection process, advocating for thorough documentation, sharing, and justification of modeling decisions to enhance reproducibility and methodological clarity in marketing research.

While we particularly invite submissions from the partial least squares structural equation modeling (PLS-SEM) research community, we welcome contributions from related fields that engage with methodological and conceptual dimensions of model selection. The special issue does not seek empirical applications (except for methods illustrations) since it primarily targets theoretical and methodological contributions rather than empirical applications.

Submission Guidelines

Authors should submit manuscripts of between 8,000–10,000 words (excluding tables, references, captions, footnotes and endnotes). All submissions must strictly follow the guidelines for the Journal of Marketing Management. These are available at
https://www.tandfonline.com/action/authorSubmission?show=instructions&journalCode=rjmm20
Submissions which do not follow these guidelines will be returned to authors for correction prior to being passed to the SI Editors.

Please note the requirements to include a Summary Statement of Contribution, and to place figures and tables at their correct location within the text. Please also read the following guidelines prior to submitting your manuscript:

Manuscripts should be submitted online using the T&F Submission Portal for Journal of Marketing Management:
https://rp.tandfonline.com/submission/create?journalCode=RJMM
Authors should prepare and upload two versions of their manuscript (only use alpha-numeric characters or underscores in the filename). One should be a complete text, while in the second all document information identifying the author should be removed from the files to allow them to be sent anonymously to referees.

When uploading files authors will be able to define the non-anonymous version as “Manuscript – with author details”, and the anonymous version as “Manuscript – Anonymous”. To submit your manuscript to the Special Issue choose “Research Article” from the Manuscript Type list in the Submission Portal. On the next screen (Manuscript Details), answer ‘yes’ to the question ‘Are you submitting your paper for a specific special issue or article collection?’. A drop down menu will then appear and you should select the Special Issue Title from this list.

Informal queries regarding guest editors’ expectations or the suitability of specific research topics should be directed to the Special Issue Editors. Please send your questions to all guest editors in a single email.

Guest Editor Contact Details

Key Dates

  • The online system will be open for submissions to this issue from 1 May 2026.
  • The closing date for submissions is 1 June 2026.

Technical queries about submissions can be referred to the Editorial Office: rjmmeditorial@westburn.co.uk

References

Adler, S. J., Röseler, L., & Schöniger, M. K. (2023). A Toolbox to Evaluate the Trustworthiness of Published Findings. Journal of Business Research, 167, 114189. https://doi.org/https://doi.org/10.1016/j.jbusres.2023.114189

Adler, S. J., Sharma, P. N., & Radomir, L. (2023). Toward Open Science in PLS-SEM: Assessing the State of the Art and Future Perspectives. Journal of Business Research, 169, 114291. https://doi.org/https://doi.org/10.1016/j.jbusres.2023.114291

Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261-304. https://doi.org/10.1177/0049124104268644

Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM Methods for Research in Social Sciences and Technology Forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/https://doi.org/10.1016/j.techfore.2021.121092

Deer, L., Adler, S.J., Datta, H., Mizik, N., & Sarstedt, M. (2025). Toward Open Science in Marketing Research. International Journal of Research in Marketing, 42(1), 212-333. https://doi.org/10.1016/j.ijresmar.2024.12.005

Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and Explanation in Social Systems. Science, 355(6324), 486-488. https://doi.org/10.1126/science.aal3856

Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., Vespignani, A., & Yarkoni, T. (2021). Integrating Explanation and Prediction in Computational Social Science. Nature, 595(7866), 181-188. https://doi.org/10.1038/s41586-021-03659-0

Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392. https://doi.org/https://doi.org/10.1111/deci.12445

Preacher, K.J. (2006). Quantifying Parsimony in Structural Equation Modeling. Multivariate Behavioral Research, 41(3), 227-259. https://doi.org/10.1207/s15327906mbr4103_1

Preacher, K.J., & Merkle, E.C. (2012). The Problem of Model Selection Uncertainty in Structural Equation Modeling. Psychological Methods, 17(1), 1-14. https://doi.org/10.1037/a0026804

Preacher, K.J., & Yaremich, H.E. (2023). Model Selection in Structural Equation Modeling. In R.H. Hoyle (Ed.). Handbook of Structural Equation Modeling 2nd ed. (206-222). New York: The Guilford Press.

Sarstedt, M., & Adler, S. J. (2023). An Advanced Method to Streamline P-hacking. Journal of Business Research, 163, 113942. https://doi.org/https://doi.org/10.1016/j.jbusres.2023.113942

Sarstedt, M., Adler, S. J., Ringle, C. M., Cho, G., Diamantopoulos, A., Hwang, H., & Liengaard, B. D. (2024). Same Model, Same Data, But Different Outcomes: Evaluating the Impact of Method Choices in Structural Equation Modeling. Journal of Product Innovation Management, 41(6), 1100-1117. https://doi.org/https://doi.org/10.1111/jpim.12738

Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive Model Assessment and Selection in Composite-based Modeling Using PLS-SEM: Extensions and Guidelines for Using CVPAT. European Journal of Marketing, 57(6), 1662-1677. https://doi.org/10.1108/EJM-08-2020-0636

Sharma, P. N., Sarstedt, M., Ringle, C. M., Cheah, J.-H., Herfurth, A., & Hair, J. F. (2024). A Framework for Enhancing the Replicability of Behavioral MIS Research Using Prediction Oriented Techniques. International Journal of Information Management, 78, 102805. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2024.102805

Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The Elephant in the Room: Evaluating the Predictive Performance of PLS Models. Journal of Business Research, 69(10), 4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189

Vandenberg, R. J., & Grelle, D. M. (2009). Alternative Model Specifications in Structural Equation Modeling. Facts, Fictions, and Truth. In C. E. Lance, & R. J. Vandenberg (Eds.), Statistical and Methodological Myths and Urban Legends. Doctrine, Verity, and Fable in the Organizational and Social Science (165-191), London: Routledge.

West, S. G., Taylor, A. B., & Wu, W. (2012). Model Fit and Model Selection in Structural Equation Modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling (209-231), New York: Guilford Press.

Zhang, J., Yang, Y., & Ding. (2023). Information Criteria for Model Selection. WIREs Computational Statistics, 15(5), 1-27. https://doi.org/10.1002/wics.1607

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