In wide range of applications for empirical data analysis, the assumption that data is collected from a single homogeneous population is often unrealistic. In particular, the identification of different groups of consumers and their appropri- ate consideration in partial least squares (PLS) path modeling constitutes a critical issue in marketing. In this work, we introduce a finite mixture PLS software imple- mentation which separates data on the basis of the estimates' heterogeneity in the inner path model. Numerical examples using experimental as well as empirical data allow the verification of the methodology's effectiveness and usefulness. The approach permits a reliable identification of distinctive customer segments along with characteristic estimates for relationships between latent variables. Researchers and practitioners can employ this method as a model evaluation technique and thereby assure that results on the aggregate data level are not affected by unobserved heterogeneity in the inner path model estimates. Otherwise, the analysis provides further indications on how to treat that problem by forming groups of data in order to perform a multi-group path analysis.

The authors propose a CTA-PLS assessment routine for measurement models. This routine applies confirmatory tetrad analysis (CTA) in a manner which is consistent with partial least squares (PLS) path modeling assumptions. The conceptualization employs a bootstrapping procedure to accomplish an appropriate statistical test examining vanishing tetrads in CTA-PLS. The approach allows distinguishing a formative indicator specification from a reflective indicator specification. Applications using experimental and empirical data demonstrate the usefulness and effectiveness of CTA-PLS. As a means of evaluating PLS path modeling results, the routine assists researchers in avoiding potentially unrepresentative consequences of measurement model misspecification.

In a method producing a polycondensate by bulk polycondensation of a monomer (or a reactant mixture) which is a fluid at the polycondensation temperature, an improvement which comprises carrying out the polycondensation until substantially the whole polycondensate becomes a solid polydispersion while continually applying a shearing force sufficient for maintaining the polycondensation system in polydispersed state at a temperature of below the sintering of the produced polycondensate but sufficiently high for allowing the polycondensation to proceed substantially.

A dynamic, physics-based model is presented for ionic polymer–metal composite (IPMC) sensors. The model is an infinite-dimensional transfer function relating the short-circuit sensing current to the applied deformation. It is obtained by deriving the exact solution to the governing partial differential equation (PDE) for the sensing dynamics, where the effect of distributed surface resistance is incorporated. The PDE is solved in the Laplace domain, subject to the condition that the charge density at the boundary is proportional to the applied stress. The physical model is expressed in terms of fundamental material parameters and sensor dimensions and is thus scalable. It can be easily reduced to low-order models for real-time conditioning of sensor signals in targeted applications of IPMC sensors. Experimental results are provided to validate the proposed model.

Summary For path modeling in business research, it is important to identify different seg- ments for the relationships between latent variables in the inner model and thereby to further differentiate model estimations. In the field of Marketing, for example, such methodologies are important to identify distinctive customer segments. A modified finite mixture is one approach that integrates customer segmentation into partial least squares based analyses of path models. To demonstrate the potentials of this methodology, experimental data is used for customer segmentation with SmartPLS. The results point out how FIMIX-PLS complements the traditional path modeling and provide information on the applicability and accurate interpre- tation of estimations.