Florian Schuberth

Partial least squares path modeling using ordinal categorical indicators

This article introduces a new consistent variance-based estimator called ordinal consistent partial least squares (OrdPLSc). OrdPLSc completes the family of variance-based estimators consisting of PLS, PLSc, and OrdPLS and permits to estimate structural equation models of composites and common factors if some or all indicators are measured on an ordinal categorical scale. A Monte Carlo simulation (N

Ordinal Consistent Partial Least Squares

=500

Confirmatory Composite Analysis

=500) with different population models shows that OrdPLSc provides almost unbiased estimates. If all constructs are modeled as common factors, OrdPLSc yields estimates close to those of its covariance-based counterpart, WLSMV, but is less efficient. If some constructs are modeled as composites, OrdPLSc is virtually without competition.

PLS path modeling: A confirmatory approach to study tourism technology and tourist behavior

Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.

The effects of person-related and environmental factors on consumers' decision-making in agri-food markets: The case of olive oils

In this chapter, we present a new variance-based estimator called ordinal consistent partial least squares (OrdPLSc). It is a promising combination of consistent partial least squares (PLSc) and ordinal partial least squares (OrdPLS), respectively, which is capable to deal in structural equation models with common factors, composites, and ordinal categorical indicators. Besides providing the theoretical background of OrdPLSc, we present three approaches to obtain constructs scores from OrdPLS and OrdPLSc, which can be used, e.g., in importance-performance matrix analysis. Finally, we show its behavior on an empirical example and provide a practical guidance for the assessment of SEMs with ordinal categorical indicators in the context of OrdPLSc.

New guidelines for the use of PLS path modeling in hospitality, travel and tourism research

In this paper we provide an extensive comparison between commonly used linear econometric methods in the audit fee literature and explicitly address their underlying assumptions. As opposed to common practice in similar papers we explicitly consider violations of the strict exogeneity assumption in terms of unobserved firm-specific effects and argue that endogeneity is likely to be present in audit fee data sets. This leads to significantly different results for magnitude and significance level of most explanatory variables. Additionally, we encourage researchers to use the in the audit fee literature not so widely-spread Hausman-Taylor estimator to benefit from its efficiency.