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Item type:Publication, Access status: without files , SEM: measurement models with composites and common factors(2020-02); ; Publicationtype: Conference Presentation182 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five RecommendationsDescriptive statistics and the application of multivariate data analysis techniques such as regression analysis and factor analysis belong to the core set of statistical instruments, and their use has generated findings that have significantly shaped the way we see the world today. The increasing reliance on and acceptance of statistical analysis, as well as the advent of powerful computer systems that allow for handling large amounts of data, paved the way for the development of more advanced next-generation analysis techniques. Structural equation modeling (SEM) is among the most useful advanced statistical analysis techniques that have emerged in the social sciences in recent decades. SEM is a class of multivariate techniques that combine aspects of factor analysis and regression, enabling the researcher to simultaneously examine relationships among measured variables and latent variables as well as between latent variables. Considering the ever-increasing importance of understanding latent phenomena such as consumer perceptions, attitudes, or intentions and their influence on organizational performance measures (e.g., stock prices), it is not surprising that SEM has become one of the most prominent statistical analysis techniques today. While there are many approaches to conducting SEM, the most widely applied method is certainly covariance-based SEM (CB-SEM). Since its introduction by Karl Jöreskog in 1973, CB-SEM has received considerable interest among empirical researchers across virtually all social sciences disciplines. Recently, however, partial least squares SEM (PLS-SEM) has gained massive attention in the social sciences as an alternative means to estimate relationships among multiple latent variables, each measured by a number of manifest variables. Along with the ongoing development of both SEM techniques, research has recently witnessed an increasing debate about the relative advantages of PLS-SEM vis-á-vis other SEM methods, which resulted in the formation of two opposing camps. One group of scholars, supportive of the PLS-SEM method, has emphasized the method’s prediction-orientation and capabilities to handle complex models, small sample sizes, and formatively specified constructs. The other group has noted that PLS-SEM is not a latent variable method, producing biased and inconsistent parameter estimates, calling for the abandonment of the method. Tying in with these debates, in this manuscript, we highlight five different perspectives on comparing results from CB-SEM and PLS-SEM. These perspectives imply that the universal rejection of one method over the other is shortsighted as such a step necessarily rests on assumptions about unknown entities in a model and the parameter estimation. We argue that researchers’ functional background and adherence to a specific position in philosophy of science contribute to the confusion over which method is “right” and which one is “wrong.” Based on our descriptions, we offer five recommendations that share a common theme: The comparison of results from CB-SEM and PLS-SEM—despite considerable research interest—is misguided, capable of providing both false confidence and false concern. Instead of seeking confidence in the comparison of results from the different approaches, researchers should instead focus on more fundamental aspects of modeling, measurement, and statistical analysis.Publicationtype: Journal Article161 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and MarakasAguirre-Urreta and Marakas [Aguirre-Urreta MI, Marakas GM (2014) Research note-Partial least squares and models with formatively specified endogenous constructs: A cautionary note. Inform. Systems Res. 25(4):761-778] aim to evaluate the performance of partial least squares (PLS) path modeling when estimating models with formative endogenous constructs, but their ability to reach valid conclusions is compromised by three major flaws in their research design. First, their population data generation model does not represent "formative measurement" as researchers generally understand that term. Second, their design involves a PLS path model that is misspecified with respect to their population model. Third, although their aim is to estimate a composite-based PLS path model, their design uses simulation data generated via a factor analytic procedure. In consequence of these flaws, Aguirre-Urreta and Marakas' (2014) study does not support valid inference about the behavior of PLS path modeling with respect to endogenous formatively measured constructs.Publicationtype: Journal Article99 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Assessing heterogeneity in customer satisfaction studies: Across industry similarities and within industry differences(2011-12-01); ; ; Purpose - Revisiting Fornell et al.'s (1996) seminal study, this chapter looks at the evidence for observed and unobserved heterogeneity within data underlying the American customer satisfaction index (ACSI) model. Examining data for two specific industries (utilities and hotels) reveals only modest differences. However, we suppose that unobserved heterogeneity critically affects the results. These insights provide the basis for shaping further differentiated ACSI model analyses and more precise interpretations. Methodology/approach - This study applies the partial least squares (PLS) path modeling method and uses empirical data to estimate and compare the ACSI model results on the aggregate and industry-specific data levels. In addition, the finite mixture PLS path modeling (FIMIX-PLS) method is employed to further examine across industry similarities and within industry differences. Findings - This research uncovers unobserved heterogeneity that guides forming three segments of customers within each industry. The major segment in each industry represents customers that are fairly loyal (i.e., neither disloyal nor extremely loyal) while the other two smaller segments are not as similar across the two industries. Our study identifies substantial differences across these segments within each industry. An importance-performance map analysis illustrates these differences and provides the basis for managerial implications. Originality/value of the chapter - The unobserved heterogeneity revealed within industries in a given country (i.e., the US) underlines the need to be open to differences within populations, beyond the observed heterogeneity across distinct groups or cultures, and the need to reconsider reporting requirements in academic research.Publicationtype: Journal Article158 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Structural modeling of heterogeneous data with partial least squares(2010-11-24); ; Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity - one population and one model - but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.Publicationtype: Journal Article108 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Response-based Segmentation in PLS Path Modeling : Application of FIMIX-PLS to American Customer Satisfaction Index DataPublicationtype: Conference Presentation67