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A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results. Key features: Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus. Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes. Provides step-by-step instructions of model specification and estimation, as well as detail interpretation of Mplus results. Explores different methods for sample size estimate and statistical power analysis for SEM. By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

[This book] reviews the basic concepts and applications of SEM using Mplus Version 6. ... The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. (DIPF/Orig.).

Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using Mplus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8.2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. * Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models * Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes * Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of Mplus results using real data sets * Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using Mplus. 

La modélisation par équations structurelles s’impose de plus en plus en sciences humaines, que ce soit en psychologie, en sociologie ou en sexologie. L’objectif du présent ouvrage est d’offrir aux chercheurs et aux étudiants une introduction à la syntaxe Mplus sous forme d’un guide pratique leur permettant de réaliser des analyses de base. Le logiciel Mplus se démarque par la diversité des analyses qu’il offre, sa polyvalence quant à la gestion des données (continues, ordinales, binaires, non normales, etc.), son traitement des données manquantes et sa simplicité d’utilisation. La modélisation par équations structurelles avec Mplus expose en détail plus de 15 analyses – dont l’analyse acheminatoire, la médiation simple, la modération, l’analyse de trajectoire latente, l’analyse de classes latentes et l’analyse factorielle exploratoire et confirmatoire. Il traite aussi de la gestion des données manquantes, des données discrètes et ordinales, ainsi que de l’échantillonnage complexe. Chaque chapitre est structuré de façon similaire: explication de l’analyse, rédaction de la syntaxe, interprétation de la sortie. Cet ouvrage intéressera autant l’étudiant qui découvre le logiciel que le chercheur désirant une ressource accessible pour l’accompagner lors de ses analyses. Il est un incontournable pour tous les chercheurs francophones utilisant fréquemment la modélisation par équations structurelles.

Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

An increasing number of studies indicate that microbial diversity plays a crucial role in the mediation of ecosystem multifunctionality (EMF) in natural ecosystems. However, this point remains mostly overlooked in managed ecosystems, especially in agriculture. Here, we compiled promising strategies for the targeted exploitation of the associations between microbial diversity and EMF of agricultural soils using samples from two long‐term (more than 30 years) experimental field sites in southern China. The two sites experienced a similar monsoon climate and fertilization management practices. We used high‐throughput amplicon sequencing, structural equation modelling and random forest analysis, to analyse our data and validate our hypotheses. We found that soil physiochemical properties and the C‐, N‐, P‐ and S‐cycle enzyme activities were increased with the increase in microbial diversity. Specifically, a positive linear relationship was observed between microbial diversity and EMF, which was mediated by long‐term fertilization management via changes in soil microbial communities and physiochemical properties. Random forest analysis and SEM showed that the important role of microbial diversity on EMF was maintained even when simultaneously taking multiple multifunctionality drivers (soil physiochemical properties, soil aggregation and enzymatic patterns) into account. In addition, microbial diversity, C‐cycle enzyme activity and pH value are feasible predictors of EMF; these factors were shown to be the main drivers of EMF of arable soils. Our findings suggest that there may be a limited degree of multifunctional redundancy in arable soils. The relationship we observed between microbial diversity and EMF suggests that management practices that foster more diverse soil microbial communities may have the potential to improve the functioning of agroecosystems. A plain language summary is available for this article. Plain Language Summary

PurposeThis study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing.Design/methodology/approachThe study is grounded in rich informational cues and information processing mechanisms by incorporating the elaboration likelihood model (ELM) and trust transfer theory. This study employs a mixed analytic method that incorporates structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to provide a complete picture of individual information process mechanisms in AR retailing under the tenet of ELM.FindingsThe SEM analysis results confirm the relationships between the central and peripheral route factors, information processing outcomes and eventual behavioral intentions. Moreover, all configurations revealed by the fsQCA include both central and peripheral factors. Hence, the dual routes proposed in the ELM are verified by using two distinct analytical approaches.Originality/valueThis study is pioneering in validating and contextualizing ELM theory in AR retailing. In addition, this study offers a methodological paradigm by demonstrating the application of multi-analysis in exploring consumers’ information process mechanisms in AR retailing, which offers a holistic and comprehensive view to understand consumers’ decision-making mechanisms.

Despite numerous testimonials of first movers, the underlying mechanisms of organizations' big data analytics (BDA) usage deserves close investigation. Our study addresses two essential research questions: (1) How does organizational BDA usage affect value creation? and (2) What are key antecedents of organizational-level BDA usage? We draw on dynamic capabilities theory to conceptualize BDA use as a unique information processing capability that brings competitive advantage to organizations. Furthermore, we employ the technology-organization-environment (TOE) framework to identify and theorize paths via which factors influence the actual usage of BDA. Survey data collected from 161 U.S.-based companies show that: organizational-level BDA usage affects organizational value creation; the degree to which BDA usage influences such creation is moderated by environmental dynamism; technological factors directly influence organizational BDA usage; and organizational and environmental factors indirectly influence organizational BDA usage through top management support. Collectively, these findings provide a theory-based understanding of the impacts and antecedents of organizational BDA usage, while also providing guidance regarding what managers should expect from usage of this rapidly emerging technology.