Search results

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.

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.

In recent years, the use of structural equation modeling (SEM) has become widespread in tourism and hospitality research. Because there are two different approaches to SEM (i.e., covariance-based SEM and variance-based, partial least squares SEM), this brings challenges for researchers about which SEM to use and what to report in each SEM approach. Therefore, the purpose of this chapter is to discuss the differences between CB-SEM and PLS-SEM and to provide comprehensive guidelines for researchers on how to apply each SEM. Within this context, the authors first briefly summarize the fundamentals and advantages of using SEM. Then, the authors explain in detail the major issues that should be considered when selecting between CB-SEM and PLS-SEM. Finally, to ensure rigorous research practices, the authors provide step-by-step guidelines for the application of both CB-SEM and PLS-SEM.

One popular method to assess discriminant validity in structural equation modeling is the heterotrait-monotrait ratio of correlations (HTMT). However, the HTMT assumes tau-equivalent measurement models, which are unlikely to hold for most empirical studies. To relax this assumption, the authors modify the original HTMT and introduce a new consistent measure for congeneric measurement models: the HTMT2.

The HTMT2 is designed in analogy to the HTMT but relies on the geometric mean instead of the arithmetic mean. A Monte Carlo simulation compares the performance of the HTMT and the HTMT2. In the simulation, several design factors are varied such as loading patterns, sample sizes and inter-construct correlations in order to compare the estimation bias of the two criteria.

The HTMT2 provides less biased estimations of the correlations among the latent variables compared to the HTMT, in particular if indicators loading patterns are heterogeneous. Consequently, the HTMT2 should be preferred over the HTMT to assess discriminant validity in case of congeneric measurement models.

However, the HTMT2 can only be determined if all correlations between involved observable variables are positive.

This paper introduces the HTMT2 as an improved version of the traditional HTMT. Compared to other approaches assessing discriminant validity, the HTMT2 provides two advantages: (1) the ease of its computation, since HTMT2 is only based on the indicator correlations, and (2) the relaxed assumption of tau-equivalence. The authors highly recommend the HTMT2 criterion over the traditional HTMT for assessing discriminant validity in empirical studies.

The purpose of this study is threefold: (1) to propose partial least squares path modeling (PLS-PM) as a way to estimate models containing composites of composites and to compare the performance of the PLS-PM approaches in this context, (2) to provide and evaluate two testing procedures to assess the overall fit of such models and (3) to introduce user-friendly step-by-step guidelines.

A simulation is conducted to examine the PLS-PM approaches and the performance of the two proposed testing procedures.

The simulation results show that the two-stage approach, its combination with the repeated indicators approach and the extended repeated indicators approach perform similarly. However, only the former is Fisher consistent. Moreover, the simulation shows that guidelines neglecting model fit assessment miss an important opportunity to detect misspecified models. Finally, the results show that both testing procedures based on the two-stage approach allow for assessment of the model fit.

Analysts who estimate and assess models containing composites of composites should use the authors’ guidelines, since the majority of existing guidelines neglect model fit assessment and thus omit a crucial step of structural equation modeling.

This study contributes to the understanding of the discussed approaches. Moreover, it highlights the importance of overall model fit assessment and provides insights about testing the fit of models containing composites of composites. Based on these findings, step-by-step guidelines are introduced to estimate and assess models containing composites of composites.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.