Advanced Text Analysis Techniques

When to use and how to report the results of PLS-SEM

Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

On the evaluation of structural equation models

Partial least squares structural equation modeling (PLS-SEM)

Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications – While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention. Originality/value – This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.

Context Effects in Processing of Chinese Academic Words: An Eye-Tracking Investigation

This study investigated context effects of online processing of Chinese academic words during text reading. Undergraduate participants were asked to read Chinese texts that were familiar or unfamiliar (containing physics terminology) to them. Physics texts were selected first, and then we replaced the physics terminology with familiar words; other common words remained the same in both text versions. Our results indicate that readers experienced longer rereading times and total fixation durations for the same common words in the physics texts than for the corresponding texts. Shorter gaze durations were observed for the replaced words than the physics terminology; however, the duration of participants' first fixations on these two word types did not differ from each other. Furthermore, although the participants performed similar reading paths after encountering the target words of the physics terminology and replaced words, their processing duration of the current sentences was very different. They reread the physics terminology more times and spent more reading time on the current sentences containing the physics terminology, searching for more information to aid comprehension. This study showed that adult readers seemed to successfully access each Chinese character's meaning but initially failed to access the meaning of the physics terminology. This could be attributable to the nature of the formation of Chinese words; however, the use of contextual information to comprehend unfamiliar words is a universal phenomenon.