Structural equation models (SEMs) make it possible to estimate the causal relationships, defined according to a theoretical model, linking two or more latent complex concepts, each measured through a number of observable indicators, usually called manifest variables. Traditionally, the component-based estimation of SEMs by means of partial least squares (PLS path modelling, PLS-PM) assumes homogeneity over the observed set of units: all units are supposed to be well represented by a unique model estimated on the overall data set. In many cases, however, it is reasonable to expect classes made of units showing heterogeneous behaviours to exist.Two different kinds of heterogeneity could be affecting the data: observed and unobserved heterogeneity. The first refers to the case of a priori existing classes, whereas in unobserved heterogeneity no information is available either on the number of classes or on their composition.If a group structure for the statistical units is given, the aim of the analysis is to search for any differences in the behaviours of the a priori given classes. In PLS-PM this would mean studying the effect of directly observed moderating variables, i.e. estimating as many (local) models as there are classes.Unobserved heterogeneity, instead, implies identifying classes of units (a priori unknown) having similar behaviours. Such heterogeneity is captured by an unobserved (latent) discrete moderating variable defining both the number of classes and the class membership.A new method for unobserved heterogeneity detection in PLS-PM is proposed in this paper: responsebased procedure for detecting unit segments in PLS-PM (REBUS-PLS). REBUS-PLS, according to PLS-PM features, does not require distributional hypotheses and may lead to local models that are different in terms of both structural and measurement models. An application of REBUS-PLS on real data will be shown.
Purpose: Purpose: Big data analytics (BDA) increasingly provide value to firms for robust decision making and solving business problems. The purpose of this paper is to explore information quality dynamics in big data environment linking business value, user satisfaction and firm performance.Design/methodology/approach: Design/methodology/approach: Drawing on the appraisal-emotional response-coping framework, the authors propose a theory on information quality dynamics that helps in achieving business value, user satisfaction and firm performance with big data strategy and implementation. Information quality from BDA is conceptualized as the antecedent to the emotional response (e.g. value and satisfaction) and coping (performance). Proposed information quality dynamics are tested using data collected from 302 business analysts across various organizations in France and the USA.Findings: Findings: The findings suggest that information quality in BDA reflects four significant dimensions: completeness, currency, format and accuracy. The overall information quality has significant, positive impact on firm performance which is mediated by business value (e.g. transactional, strategic and transformational) and user satisfaction.
Research limitations/implications:Research limitations/implications: On the one hand, this paper shows how to operationalize information quality, business value, satisfaction and firm performance in BDA using PLS-SEM. On the other hand, it proposes an REBUS-PLS algorithm to automatically detect three groups of users sharing the same behaviors when determining the information quality perceptions of BDA.
Practical implications:Practical implications: The study offers a set of determinants for information quality and business value in BDA projects, in order to support managers in their decision to enhance user satisfaction and firm performance.Originality/value: Originality/value: The paper extends big data literature by offering an appraisal-emotional responsecoping framework that is well fitted for information quality modeling on firm performance. The methodological novelty lies in embracing REBUS-PLS to handle unobserved heterogeneity in the sample.
PurposeBig data analytics (BDA) increasingly provide value to firms for robust decision making and solving business problems. This paper explores information quality dynamics in big data environment linking business value, user satisfaction and firm performance.
Design/Methodology/ApproachDrawing on appraisal-emotional response-coping framework, we propose a theory on information quality dynamics that helps in achieving business value, user satisfaction and firm performance with big data strategy and implementation. Information quality from BDA is conceptualized as the antecedent to the emotional response (e.g., value and satisfaction) and coping (performance). Proposed information quality dynamics are tested using data collected from 302 business analysts across various organizations in France and the US.
FindingsOur findings suggest that informat...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.