Developing models that clarify the complex relationships between antecedents and quality outcomes—such as customer-perceived quality and firm performance—is essential for advancing both theory and practice in the field of quality management. This special issue aims to explore recent advancements in modeling approaches and their applications within quality management research. While the focus is not limited to a single methodology, particular attention is given to structural equation modeling (SEM). This method offers distinct approaches for evaluating proposed models, emphasizing both explanation and prediction (e.g., CB-SEM and PLS-SEM). Recent developments highlight the potential of integrating multiple SEM approaches within multi-method designs, as well as incorporating complementary techniques such as necessary condition analysis (NCA) and combined importance–performance map analysis (cIPMA). In addition, this special issue welcomes contributions that integrate traditional modeling techniques with emerging machine learning (ML) and artificial intelligence (AI) methods. This includes advanced predictive analytics and hybrid approaches that combine SEM with ML to uncover complex data patterns. The convergence of established methodological frameworks with computational intelligence presents significant opportunities to enhance both explanatory power and predictive accuracy in quality management research. Overall, this special issue seeks to demonstrate how advanced modeling methods can enrich quality management theory and practice by providing more robust analysis tools for explanation, prediction, replication, and continuous improvement.
List of Topic Areas
Potential topics include, but are not limited to:
- Applications of SEM and multi-method SEM approaches to explain and predict quality outcomes, including customers’ and employees’ quality perceptions and well-being
- Alternative model comparison and selection techniques to support decision-making in quality management (e.g., predicting satisfaction, loyalty, and word-of-mouth)
- The use of nonlinear effects, (multiple) mediation, moderation, and moderated mediation to uncover complex relationships between quality and related constructs
- Multi-group analysis and latent class techniques (e.g., FIMIX-PLS and PLS-POS) to examine and explain differences in quality perceptions (e.g., across countries, customer segments, or stakeholder groups)
- Development and validation of measurement scales for emerging quality constructs (e.g., AI service quality, platform quality, sustainability–quality integration, omnichannel quality experience) and measurement-related issues in quality management research including formative and reflective measurement models, and higher-order constructs
- Applications of necessary condition analysis (NCA) and combined importance–performance map analysis (cIPMA) to derive actionable and evidence-based quality management recommendations
- Modeling approaches to predict the outcomes of TQM, Lean Six Sigma, Industry 4.0 and 5.0 initiatives, artificial intelligence implementation, new technology adoption, and related transformations
- Integration of SEM with machine learning algorithms and other advanced modeling techniques to address quality management challenges
- Comparative assessments of alternative modeling approaches for specific research questions in quality management
- Other methodological advancements and innovative applications of modeling techniques in quality management across industries and countries.
Guest Editors
Fabio Cassia, University of Verona (Italy) fabio.cassia@univr.it
Francesca Magno, University of Bergamo (Italy) francesca.magno@unibg.it
Miriana Ferrara, University of Salerno (Italy) mferrara@unisa.it
Christian M. Ringle, Hamburg University of Technology (Germany) and James Cook University (Australia), c.ringle@tuhh.de
Submissions Information
Submissions are made using ScholarOne Manuscripts. Author guidelines must be strictly followed.
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”. Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Key Deadlines
Opening date for manuscripts submissions: 15th November 2026
Closing date for manuscripts submission: 15th January 2027
For more details refer here
