PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research

Exploring theoretically plausible alternative models for explaining the phenomenon under study is a crucial step in advancing scientific knowledge. This paper advocates model selection in information systems (IS) studies that use partial least squares path modeling (PLS) and suggests the use of model selection criteria derived from information theory for this purpose. These criteria allow researchers to compare alternative models and select a parsimonious yet well-fitting model. However, as our review of prior IS research practice shows, their use—while common in the econometrics field and in factor-based SEM—has not found its way into studies using PLS. Using a Monte Carlo study, we compare the performance of several model selection criteria in selecting the best model from a set of competing models under different model set-ups and various conditions of sample size, effect size, and loading patterns. Our results suggest that appropriate model selection cannot be achieved by relying on the PLS criteria (i.e., R2, Adjusted R2, GoF, and Q2), as is the current practice in academic research. Instead, model selection criteria—in particular, the Bayesian information criterion (BIC) and the Geweke-Meese criterion (GM)—should be used due to their high model selection accuracy and ease of use. To support researchers in the adoption of these criteria, we introduce a five-step procedure that delineates the roles of model selection and statistical inference and discuss misconceptions that may arise in their use

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Estimation issues with PLS and CBSEM: where the bias lies!

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about themeaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective,we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based

Taylor-dispersion induced phase separation for efficient characterisation of protein condensate formation

Biomolecular condensates have emerged as important structures in cellular function and disease, and are thought to form through liquid-liquid phase separation (LLPS). Thorough and efficient in vitro experiments are therefore needed to elucidate the driving forces of protein LLPS and the possibility to modulate it with drugs. Here we present Taylor dispersion induced phase separation (TDIPS), a method to robustly measure condensation phenomena using a commercially available microfluidic platform. It uses only nano-liters of sample, does not require extrinsic fluorescent labels, and is straightforward to implement. We demonstrate TDIPS by screening the phase behaviour of two proteins that form biomolecular condensates in vivo, PGL-3 and Ddx4. Uniquely accessible to this method, we find an unexpected re-entrant behaviour at very low ionic strength, where LLPS is inhibited for both proteins. TDIPS can also probe the reversibility of assemblies, which was shown for both α-synuclein and for lysozyme, relevant for health and biotechnology, respectively. Finally, we highlight how effective inhibition concentrations and partitioning of LLPS-modifying compounds can be screened highly efficiently

Aberrant Collagenase Expression in Chronic Idiopathic Myelofibrosis Is Related to the Stage of Disease but Not to the JAK2 Mutation Status

Bone marrow fibrosis in chronic idiopathic myelofibrosis (cIMF) most likely represents an imbalance between synthesis and turnover of collagen fibers. Because the JAK-STAT signaling pathway is involved in the regulation of genes encoding matrix metalloproteinases (MMPs), we examined the expression of MMPs, their tissue inhibitors (TIMPs), and collagen types in relation to the JAK2 status (V617F mutation versus wild-type) in cIMF (n = 64). Whereas no correlation was found between the JAK2 status and MMP gene products, there was an evident association with the stage of disease. Membrane type 1-MMP (MMP-14) was overexpressed by up to 80-fold in advanced stages that progressed to fibrosis (P < 0.001), and megakaryocytes and endothelial cells were unmasked as the major cellular source. By contrast, a significantly higher expression of neutrophil collagenase (MMP-8) was encountered in the prefibrotic stages of cIMF (P < 0.001). Altogether, the stepwise progress of myelofibrosis in cIMF was associated with expression of a defined subset of target genes as shown in sequential trephine biopsies of cIMF patients. We conclude that the expression of matrix-modeling genes in cIMF is not influenced by the JAK2 mutation status but is predominantly related to the stage of disease