Confirmatory composite analysis (CCA) is a subtype of structural equation modeling that assesses composite models. Composite models consist of a set of interrelated emergent variables, i.e., ...constructs which emerge as linear combinations of other variables. Only recently, Hair et al. (J Bus Res 109(1):101–110, 2020) proposed ‘confirmatory composite analysis’ as a method of confirming measurement quality (MCMQ) in partial least squares structural equation modeling. As a response to their study and to prevent researchers from confusing the two, this article explains what CCA and MCMQ are, what steps they entail and what differences they have. Moreover, to demonstrate their efficacy, a scenario analysis was conducted. The results of this analysis imply that to assess composite models, researchers should use CCA, and to assess reflective and causal–formative measurement models, researchers should apply structural equation modeling including confirmatory factor analysis instead of Hair et al.’s MCMQ. Finally, the article offers a set of corrections to the article of Hair et al. (2020) and stresses the importance of ensuring that the applied model assessment criteria are consistent with the specified model.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•PLS-PM has been subject to many improvements in last years.•Prior PLS guidelines have not covered the entire recent developments.•We explain how to perform and report an up-to-date empirical ...analysis with PLS.•We provide a fictive illustrative example on business value of social media.
Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Confirmatory Composite Analysis Schuberth, Florian; Henseler, Jörg; Dijkstra, Theo K
Frontiers in psychology,
12/2018, Volume:
9
Journal Article
Peer reviewed
Open access
This article introduces confirmatory composite analysis (CCA) as a structural equation modeling technique that aims at testing composite models. It facilitates the operationalization and assessment ...of design concepts, so-called artifacts. CCA entails the same steps as confirmatory factor analysis: model specification, model identification, model estimation, and model assessment. Composite models are specified such that they consist of a set of interrelated composites, all of which emerge as linear combinations of observable variables. Researchers must ensure theoretical identification of their specified model. For the estimation of the model, several estimators are available; in particular Kettenring's extensions of canonical correlation analysis provide consistent estimates. Model assessment mainly relies on the Bollen-Stine bootstrap to assess the discrepancy between the empirical and the estimated model-implied indicator covariance matrix. A Monte Carlo simulation examines the efficacy of CCA, and demonstrates that CCA is able to detect various forms of model misspecification.
Recently, a study compared the effect size and statistical power of covariance-based structural equation modeling (CB-SEM) and path analysis using various types of composite scores (Deng, L., & Yuan, ...K.-H.,
Behavior Research Methods,
55
, 1460–1479, 2023). This comparison uses nine empirical datasets to estimate eleven models. Based on the meta-comparison, that study concludes that path analysis via weighted composites yields “path coefficients with less relative errors, as reflected by greater effect size and statistical power” (ibidem, p. 1475). In our paper, we object to this central conclusion. We demonstrate that the justification these authors provided for comparing CB-SEM and path analysis via weighted composites is not well grounded. Similarly, we explain that their employed study design, i.e., a meta-comparison, is very limited in its ability to compare the effect size and power delivered across these methods. Finally, we replicated Deng and Yuan’s (ibidem) meta-comparison and show that CB-SEM using the normal-distribution-based maximum likelihood estimator does not necessarily deliver smaller effect sizes than path analysis via composites if a different scaling method is employed for CB-SEM.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Research in human development often relies on composites, that is, composed variables such as indices. Their composite nature renders these variables inaccessible to conventional factor-centric ...psychometric validation techniques such as confirmatory factor analysis (CFA). In the context of human development research, there is currently no appropriate technique available for assessing composites with the same degree of rigor comparable to that known from CFA. As a remedy, this article presents confirmatory composite analysis (CCA), a statistical approach suitable to assess composites. CCA is a special type of structural equation modeling that consists of model specification, model identification, model estimation, and model assessment. This article explains CCA and its steps. In addition, it illustrates CCA’s use by means of an illustrative example.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Structural equation modeling (SEM) is a versatile statistical method that should theoretically be able to emulate all other methods that are based on the general linear model. In practice, however, ...researchers using SEM encounter problems incorporating composites into their models. In this tutorial article, I present a specification for SEM that was recently sketched by Henseler (2021) to incorporate composites in structural models. It draws from the same idea that was proposed in the c`ontext of canonical correlation analysis to express a set of observed variables forming a composite by a set of synthetic variables (Ogasawara, 2007), which were labeled by Henseler (2021) as emergent and excrescent variables. An emergent variable is a linear combination of variables that is related to other variables in the structural model, whereas an excrescent variable is a linear combination of variables that is unrelated to all other variables in the structural model. This approach is advantageous over existing approaches, as it allows drawing on all existing developments in SEM, such as testing parameter estimates, testing for overall model fit and dealing with missing values. To demonstrate the presented approach, I conduct a small scenario analysis. Moreover, SEM applying the presented specification is used to reestimate an empirical example from Hwang et al. (2021). Finally, this article discusses avenues for future research opened by this approach for SEM to study composites.
Translational abstractStructural equation modeling (SEM) is a statistical method that offers researchers great flexibility in specifying their models. Yet, researchers face difficulties incorporating composite into their models. To address this issue, in this tutorial paper I present the Henseler-Ogasawara (H-O) specification for composites, which gives researchers the same flexibility that they are accustomed from modeling with latent variables. The H-O specification draws from the idea to express a set of observed variables forming a composite by a set of emergent and excrescent variables. An emergent variable is a linear combination of variables that is related to other variables in the structural model, whereas an excrescent variable is a linear combination of variables that is unrelated to all other variables in the structural model. This approach is advantageous over existing approaches, as it allows drawing on all existing developments in SEM, such as testing parameter estimates, testing for overall model fit and dealing with missing values.
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CEKLJ, FFLJ, NUK, ODKLJ, PEFLJ, UPUK
COVID-19 made evident the need for workplace digital transformation due to a rapid transition from office to remote work. Therefore, employers must make telework suitable for office workers who ...suddenly became permanent teleworkers. By using partial least squares path modeling, this article suggests the defining of telework tasks suitability and of telework workplace suitability by performing an empirical study with 691 employees who had experienced a rapid transition from office work to remote work during the pandemic. Both telework tasks suitability and telework workplace suitability are found to have a positive relationship with collaboration and work performance. Employers should therefore especially focus on communication technology when expecting employees work from home to improve work performance and enable collaboration to prevent them from feeling isolated. This study is the first to define telework tasks suitability and workplace suitability for enabling collaboration and improving work performance of teleworkers after an enforced transition from office working to teleworking.
Abstract
The pragmatic view of urban resilience has re-framed long-lasting social issues as chronic social stresses that can be addressed by building strong social networks in urban environments. ...This practice, inspired by disaster management, is problematic because it presupposes a community whose members share the same fate. Conversely, social vulnerability emerges from the asymmetrical distribution of agency in the social order, so that a low social position jeopardises life chances. Hence, we argue that the social dimension in urban resilience should focus on the role of social positions and individuals’ agentic predispositions to control their life chances if faced with adversity (i.e., their Mastery). Using structural equation modelling and data from a 2018 public Dutch survey, we found that when mediated by Mastery, socioeconomic status drives the individual’s positive adaptation behaviour. In contrast, Interaction with Primary Networks, Neighbourhood Cohesion, and Membership in Voluntary Associations have an unsubstantial relationship to positive adaptation. These empirical results suggest that Mastery is crucial for people’s resilience in their daily life. In view of the recent shift towards negotiation in resilience thinking, we propose Mastery as the guiding factor for transforming arrangements that shape social positions.
•Like latent variables, emergent variables serve to model abstract concepts.•Emergent variables can be assessed using confirmatory composite analysis (CCA).•CCA works in analogy to confirmatory ...factor analysis (CFA).•CCA deals with composite models, not reflective or formative measurement models.•The presentation of CCA by Hair et al. (2020) is misguided.
Confirmatory composite analysis (CCA) was invented by Jörg Henseler and Theo K. Dijkstra in 2014 and elaborated by Schuberth et al. (2018b) as an innovative set of procedures for specifying and assessing composite models. Composite models consist of two or more interrelated constructs, all of which emerge as linear combinations of extant variables, hence the term ‘emergent variables’. In a recent JBR paper, Hair et al. (2020) mistook CCA for the measurement model evaluation step of partial least squares structural equation modeling. In order to clear up potential confusion among JBR readers, the paper at hand explains CCA as it was originally developed, including its key steps: model specification, identification, estimation, and assessment. Moreover, it illustrates the use of CCA by means of an empirical study on business value of information technology. A final discussion aims to help analysts in business research to decide which type of covariance structure analysis to use.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PurposeOne 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsHowever, the HTMT2 can only be determined if all correlations between involved observable variables are positive.Originality/valueThis 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.
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