As email use becomes more ubiquitous in organisations, negative effects that stem from its use are becoming more prevalent. This study considers Email Overload as a negative product of email use. It explores the link between the personality traits of Self-esteem and Locus of Control and Email Overload. Furthermore it proposes a link between the level of perceived Email Overload and individual p...

Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often fac...

This study analyzes the effects of several post-adoption behaviors (extent of use, routinization and infusion) on overall performance in using an Electronic Document Management System (EDMS). Furthermore, we test whether the routinization and infusion variables mediate the influence of the extent of use on overall performance. This research collects data from a survey answered by 2,175 employee...

Most IS studies considered post-adoption behavior as a cognitive process but rarely took a habitual perspective. The present study developed a research model to investigate the antecedents and effects of users’ habit in the context of social networking websites (SNW). It used a two-stage survey and partial least squares (PLS) analysis to test the model. It found that a user’s habit of using an ...

Partial Least Squares Regression (PLSR) is a linear regression technique developed to deal with high-dimensional regressors and one or several response variables. In this paper we introduce robustified versions of the SIMPLS algorithm being the leading PLSR algorithm because of its speed and efficiency. Because SIMPLS is based on the empirical cross-covariance matrix between the response variab...

We present a method for computing partial spectra of Hermitian matrices, based on a combination of subspace iteration with rational filtering. In contrast with classical rational filters derived from Cauchy integrals or from uniform approximations to a step function, we adopt a least-squares (LS) viewpoint for designing filters. One of the goals of the proposed approach is to build a filter tha...

In the machine learning field, feature selection is used to discard the redundant information and improve the learning accuracy. In this paper, the redundant information is reused in the learning of partial least squares method within the frame of multitask learning. This newly proposed method is used to solve the multivariate calibration problem, a classic problem in the analytical chemistry f...

The main contributions of this paper can be summarized as follows. First, we compare the Partial Least Squares (PLS) and the Principal Component Analysis (PCA), under fairly general conditions. (In particular, the existence of a true linear regression is not assumed.) We prove that PLS and PCA are equivalent, to within a rst-order approximation, hence providing a theoretical explanation for emp...

Partial Least Squares (PLS) is a widely used technique in chemometrics, especially in the case where the number of independent variables is significantly larger than the number of data points. There are many articles on PLS [HTF01, GK86] but the mathematical details of PLS do not always come out clearly in these treatments. This paper is an attempt to describe PLS in precise and simple mathemat...

In this work we find out how PLS algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had until now been totally unexplored, allowed us to devise a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. Mots-clès: Analyse des données data mining, Problèmes inverses et sparsité