•Both methods are equally effective for developing and analyzing the structural relationship.•CB-SEM demands a lot from the data, whereas PLS-SEM is quite lenient.•For a factor-based model, CB-SEM ...should be used.•For a composite-based model, PLS-SEM should be considered.•CB-SEM and PLSc-SEM methods provide almost similar results.
This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is based on covariance, and the second one is based on variance (partial least squares). It further assesses the difference between PLS and Consistent PLS algorithms. To assess the same, empirical data is used. Four hundred sixty-six respondents from India, Saudi Arabia, South Africa, the USA, and few other countries are considered. The structural model is tested with the help of both approaches. Findings indicate that the item loadings are usually higher in PLS-SEM than CB-SEM. The structural relationship is closer to CB-SEM if a consistent PLS algorithm is undertaken in PLS-SEM. It is also found that average variance extracted (AVE) and composite reliability (CR) values are higher in the PLS-SEM method, indicating better construct reliability and validity. CB-SEM is better in providing model fit indices, whereas PLS-SEM fit indices are still evolving. CB-SEM models are better for factor-based models like ours, whereas composite-based models provide excellent outcomes in PLS-SEM. This study contributes to the existing literature significantly by providing an empirical comparison of all the three methods for predictive research domains. The multi-national context makes the study relevant and replicable universally. We call for researchers to revisit the widely used SEM approaches, especially using appropriate SEM methods for factor-based and composite-based models.
Although the number of empirical applications of partial least–squares structural equation modeling (PLS-SEM) in tourism has increased in the last two years, Assaker, Huang, and Hallak have conducted ...the only assessment on the use of PLS-SEM in four studies and with a limited number of criteria. Thus, this study aims to critically analyze how the PLS-SEM method has been applied in 44 articles published in 11 leading tourism journals from 2000 to 2014 in terms of four key criteria: (1) themes explored and main motivations for using PLS-SEM; (2) characteristics of proposed models; (3) how the models were evaluated; and (4) the use of more advanced analyses within the method. The findings revealed that although applications in tourism have improved in recent years, problematic aspects in the application of PLS-SEM in tourism research still exist. The article provides suggestions on how to improve the use of PLS-SEM in future tourism applications.
Summary
We conduct a meta‐analytic review that yields important insights about the existing research on transformational leadership and creativity. Additionally, we propose and test an integrated ...model using meta‐analytic structural equation modeling (MASEM) and full information MASEM (FIMASEM) techniques to better understand the intervening mechanism through which transformational leadership acts on creativity. The results of the meta‐analysis of 127 studies show that most of the bivariate relationships among transformational leadership, employee creativity, and pre‐identified mediators are significant; further, geographic base of studies significantly moderates some of the relationships. The MASEM results indicate that several mediators intervene in the relationship between transformational leadership and creativity. Although the total effect of transformational leadership on creativity is positive, its direct effect is negative when mediators are included. Additionally, there are significant relationships among the mediators that can be theoretically supported, but have not been investigated in prior transformational leadership and creativity studies. On the basis of these findings, we provide conclusions and directions for future studies.
This study investigated the effect the number of observed variables (p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root ...mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading.
Structural equation modeling (SEM) is a widely applied and useful tool for project management scholars. In this Thoughtlet article, we critically reflect on the measurement philosophy underlying the ...two streams of SEM and their adequacy for estimating relationships among concepts commonly encountered in the field (e.g., team performance). We also discuss considerations to ponder when making the choice between the two types of SEM as well as between SEM and regression analysis.
Research Summary
A firm's strategic orientation has long been of interest in management and strategy research. In particular, entrepreneurial, market, and learning orientations have received thorough ...theoretical and empirical research attention. In this meta‐analysis, we compare the direct and combined performance effects of these orientations, explore their interrelatedness, and provide a theoretical foundation for complementarity between the three. Building on prior empirical findings from 210 samples and using structural equation modeling and seemingly unrelated regression techniques, we extend the knowledge base on strategic orientations. Our results provide evidence for interrelatedness and complementarity among strategic orientations, indicating that superior firm performance emerges from its capability to align entrepreneurial, market, and learning orientations.
Managerial Summary
Managers might be tempted to divide rather than combine their attention on various aspects of strategy, such as entrepreneurial, market, and learning orientations. Similarly, organizational culture might inhibit or promote collaboration between distinct organizational functions. We synthesize a vast body of research on firm‐level strategy making and reveal that while each strategic orientation is beneficial on its own, together, the three strategic orientations create synergies that surpass the effects of individual strategic orientations. Therefore, to achieve superior performance, firms need to align their strategy making efforts to (a) monitoring changes in customer needs and competitor moves, (b) engaging in creative processes, and (c) assimilating the extensive knowledge gained from these activities.
The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R ...statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as structural equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.
Early meta‐analyses in management research sought primarily to resolve seemingly conflicting findings by estimating a relationship’s population‐level effect size. Since then, management researchers ...have adopted increasingly sophisticated approaches that permit new theorizing, testing and comparing sophisticated models, and identifying boundary conditions. We summarize three of these approaches – i.e., qualitative meta‐analysis (QMA), meta‐analytic structural equation modeling (MASEM), and meta‐analytic regression analysis (MARA) – along with the special issue papers that adopt each approach. We conclude by raising three unresolved controversies that we believe deserve more attention and by offering our thoughts about how to maximize a meta‐analytic study’s chances for publication and impact.
This article compares two missing data procedures, full information maximum likelihood (FIML) and multiple imputation (MI), to investigate their relative performances in relation to the results from ...analyses of the original complete data or the hypothetical data available before missingness occurred. By expressing the FIML estimator as a special MI estimator, we predicted the expected patterns of discrepancy between the two estimators. Via Monte Carlo simulation studies where we have access to the original complete data, we compare the performance of FIML and MI estimators to that of the complete data maximum likelihood (ML) estimator under a wide range of conditions, including differences in sample size, percent of missingness, and degrees of model misfit. Our study confirmed well-known knowledge that the two estimators tend to yield essentially equivalent results to each other and to those from analysis of complete data when the postulated model is correctly specified. However, some noteworthy patterns of discrepancies were found between the FIML and MI estimators when the hypothesized model does not hold exactly in the population: MI-based parameter estimates, comparative fit index (CFI), and the Tucker Lewis index (TLI) tend to be closer to the counterparts of the complete data ML estimates, whereas FIML-based chi-squares and root mean square error of approximation (RMSEA) tend to be closer to the counterparts of the complete data ML estimates. We explained the observed patterns of discrepancy between the two estimators as a function of the interplay between the parsimony and accuracy of the imputation model. We concluded by discussing practical and methodological implications and issues for further research.
Translational Abstract
In this article, two classes of modern missing data procedures, maximum likelihood (ML) and multiple imputation (MI), are systematically compared. Although it has been argued that the two classes of missing data procedures are essentially equivalent, we showed that they may not produce equivalent results as practiced empirically under realistic conditions where researchers work with imperfect models. Following a review of relevant estimation theory, we have made specific a priori predictions on the expected patterns of the discrepancy between FIML and MI estimators, with respect to parameter estimates, their associated sampling variabilities, and the goodness of fit indices. Via Monte Carlo simulation studies where we have access to the original complete data, we showed that the two classes of procedures exhibit subtle but important differences in their performance in relation to the results from complete data analysis. Based on our theoretical predictions and the observed patterns, we provided a few practical recommendations and directions for future research.
Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial ...least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.
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