Model Selection in Marketing Research
Introduction
Special issue of the Journal of Marketing Management; Deadline 1 Jun 2026
INTEREST CATEGORY: MARKETING RESEARCH
POSTING TYPE: Calls: Journals
Posted by: Marko Sarstedt
Call for papers
Journal of Marketing Management
Model Selection in Marketing Research
Special Issue Editors
Marko Sarstedt, Ludwig-Maximilians University Munich, Germany and Babe-Bolyai University, Romania, sarstedt@lmu.de
Francesca Magno, University of Bergamo, Italy, francesca.magno@unibg.it
Vasilica-Maria Margalina, CESINE University Center, Spain, vasilicamaria.margalina@campuscesine.com
Fabio Cassia, University of Verona, Italy, fabio.cassia@unibg.it
Christian M. Ringle, Hamburg University of Technology, Germany and James Cook University, Australia, c.ringle@tuhh.de
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.
Introduction and Background
Models simplify reality to reveal key relationships. While they cant 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 models 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 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 include:
- 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 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.
Further Information and Submission Guidelines