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The purpose of this paper is to integrate the findings of articles appearing in European Journal of Marketing’s special section on covariance-based versus composite-based structural equations modeling (SEM).

This is an editorial which uses literature review to draw conclusions regarding areas of agreement, areas for further research, and changing the discussion around composite-based SEM methods.

There are now four new areas of agreement regarding composite-based SEM. Researchers should adopt a toolbox approach to their methods and know the strengths and weaknesses of the research tools in their toolbox. Partial least squares (PLS) SEM and covariance-based SEM are not substitutes, and it is inappropriate to use the language of confirmatory factor analysis (CFA) in reporting measurement estimates from PLS SEM. Measurement matters and researchers need to devote effort to using reliable and valid multi-item measures in their investigations.

This postscript article outlines recommendations for authors, reviewers and editors regarding the analysis of data and reporting of results using structural equations models.

The purpose of this study is to provide an overview of emerging prediction assessment tools for composite-based PLS-SEM, particularly proposed out-of-sample prediction methodologies.

A review of recently developed out-of-sample prediction assessment tools for composite-based PLS-SEM that will expand the skills of researchers and inform them on new methodologies for improving evaluation of theoretical models. Recently developed and proposed cross-validation approaches for model comparisons and benchmarking are reviewed and evaluated.

The results summarize next-generation prediction metrics that will substantially improve researchers' ability to assess and report the extent to which their theoretical models provide meaningful predictions. Improved prediction assessment metrics are essential to justify (practical) implications and recommendations developed on the basis of theoretical model estimation results.

The paper provides an overview of recently developed and proposed out-of-sample prediction metrics for composite-based PLS-SEM that will enhance the ability of researchers to demonstrate generalization of their findings from sample data to the population.

To overcome the problem of inferior through-the-thickness mechanical properties displayed by armor-grade composites based on 2-D reinforcement architectures, armor-grade composites based on 3D fiber-reinforcement architectures have recently been investigated experimentally.

The subject of the present work is armor-grade composite materials reinforced using ultra-high-molecular-weight polyethylene fibers and having four (two 2D and two 3D) prototypical architectures, as well as the derivation of the corresponding material models. The effect of the reinforcement architecture is accounted for by constructing the appropriate unit cells (within which the constituent materials and their morphologies are represented explicitly) and subjecting them to a series of virtual mechanical tests. The results obtained are used within a post-processing analysis to derive and parameterize the corresponding homogenized-material models. One of these models (specifically, the one for 0°/90° cross-collimated fiber architecture) was directly validated by comparing its predictions with the experimental counterparts. The other models are validated by examining their physical soundness and details of their predictions. Lastly, the models are integrated as user-material subroutines, and linked with a commercial finite-element package, in order to carry out a transient non-linear dynamics analysis of ballistic transverse impact of armor-grade composite-material panels with different reinforcement architectures.

It is found that the reinforcement architecture plays a critical role in the overall ballistic limit of the armor panel, as well as in its structural and damage/failure response.

To the authors’ knowledge, the present work is the first reported attempt to assess, computationally, the utility and effectiveness of 3D fiber-reinforcement architectures for ballistic impact applications.

Highlighting supplier collaboration is a widely adopted practice in tea shops, cafes and other related businesses. However, few studies have examined the effects of supplier-focused food ingredient presentation on consumers' behavioral intentions in food and hospitality research. This study identified the role of restaurant–supplier co-creation and explored its effects on clients' behavioral intention through both value perception and image.

This study developed and tested a model through a survey with 365 responses, which was carried out in China. The measurement and effects of restaurant–supplier co-creation are explored using composite-based structural equation modeling.

The results show that restaurant–supplier co-creation has a positive effect on perceptions of food value, service value and image. Though restaurant–supplier co-creation does not directly affect behavioral intention, it can enhance behavioral intention by improving customers' impressions.

This study contributes to the emerging literature by conceptualizing the role of restaurant–supplier co-creation. The research combines image theory and perceived value theory in one empirical model to explore the effects of co-creation. The project provides a paradigm for designing technical artifacts using composite-based structural equation modeling.

Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.

Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.

This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.

The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.

Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.

This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.

The purpose of this paper is to propose a new method of synthesis of CuO · SiO₂ oxide composite based on the reaction of precipitation from water solutions of sodium silicate and copper nitrate.

Solutions of sodium silicate and copper nitrate were used as substrates. The effects of direction of substrate supply, concentration, excess of reagents and temperature of precipitation on the physicochemical properties of the products were analysed.

A new method of synthesis of CuO · SiO₂ oxide composite based on a precipitation reaction is proposed.

Only sodium silicate and copper nitrate solutions were used.

The CuO · SiO₂ oxide composite obtained can be used as blue pigment or polymer filler.

The paper determines optimum conditions of CuO · SiO₂ oxide composite precipitation to obtain products with desired physicochemical, dispersive and structural properties.

This study aims to investigate the tensile strength and elastic modulus of custom-designed polymer composites developed using voxel-based design. This study also evaluates theoretical models, such as the rule of mixtures, Halpin–Tsai model, Cox–Krenchel model and the Young–Beaumont model and the ability to predict the mechanical properties of particle-reinforced composites based on changes in the design of rigid particles at the microscale within a flexible polymer matrix.

This study leverages the PolyJet process for voxel-printing capabilities and a design of experiments approach to define the microstructural design elements (i.e. aspect ratio, orientation, size and volume fraction) used to create custom-designed composites.

The comparison between the predictions and experimental results helps identify appropriate methods for determining the mechanical properties of custom-designed composites ensuring informed design decisions for improved mechanical properties.

This work centers on multimaterial additive manufacturing leveraging design freedom and material complexity to create a wide range of composite materials. This study highlights the importance of identifying the process, structure and property relationships in material design.

Traditionally, an armor-grade composite is based on a two-dimensional (2D) architecture of its fiber reinforcements. However, various experimental investigations have shown that armor-grade composites based on 2D-reinforcement architectures tend to display inferior through-the-thickness mechanical properties, compromising their ballistic performance. To overcome this problem, armor-grade composites based on three-dimensional (3D) fiber-reinforcement architectures have recently been investigated experimentally. The paper aims to discuss these issues.

In the present work, continuum-level material models are derived, parameterized and validated for armor-grade composite materials, having four (two 2D and two 3D) prototypical reinforcement architectures based on oriented ultra-high molecular-weight polyethylene fibers. To properly and accurately account for the effect of the reinforcement architecture, the appropriate unit cells (within which the constituent materials and their morphologies are represented explicitly) are constructed and subjected to a series of virtual mechanical tests (VMTs). The results obtained are used within a post-processing analysis to derive and parameterize the corresponding homogenized-material models. One of these models (specifically, the one for 0°/90° cross-collimated fiber architecture) was directly validated by comparing its predictions with the experimental counterparts. The other models are validated by examining their physical soundness and details of their predictions. Lastly, the models are integrated as user-material subroutines, and linked with a commercial finite-element package, in order to carry out a transient non-linear dynamics analysis of ballistic transverse impact of armor-grade composite-material panels with different reinforcement architectures.

The results obtained clearly revealed the role the reinforcement architecture plays in the overall ballistic limit of the armor panel, as well as in its structural and damage/failure response.

To the authors’ knowledge, the present work is the first reported attempt to assess, computationally, the utility and effectiveness of 3D fiber-reinforcement architectures for ballistic-impact applications.