Florida Atlantic University

About: Florida Atlantic University is a education organization based out in Boca Raton, Florida, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7788 authors who have published 19830 publications receiving 535694 citations. The organization is also known as: FAU & Florida Atlantic.

Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management

Abstract: We develop and test a theoretical model to investigate the assimilation of enterprise systems in the post-implementation stage within organizations. Specifically, this model explains how top management mediates the impact of external institutional pressures on the degree of usage of enterprise resource planning (ERP) systems. The hypotheses were tested using survey data from companies that have already implemented ERP systems. Results from partial least squares analyses suggest that mimetic pressures positively affect top management beliefs, which then positively affects top management participation in the ERP assimilation process. In turn, top management participation is confirmed to positively affect the degree of ERP usage. Results also suggest that coercive pressures positively affect top management participation without the mediation of top management beliefs. Surprisingly, we do not find support for our hypothesis that top management participation mediates the effect of normative pressures on ERP usage, but instead we find that normative pressures directly affect ERP usage. Our findings highlight the important role of top management in mediating the effect of institutional pressures on IT assimilation. We confirm that institutional pressures, which are known to be important for IT adoption and implementation, also contribute to post-implementation assimilation when the integration processes are prolonged and outcomes are dynamic and uncertain.

A survey of transfer learning

Abstract: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.

Data mining with big data

Abstract: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.

Understanding and mitigating uncertainty in online exchange relationships: a principal- agent perspective

Abstract: Despite a decade since the inception of B2C e-commerce, the uncertainty of the online environment still makes many consumers reluctant to engage in online exchange relationships. Even if uncertainty has been widely touted as the primary barrier to online transactions, the literature has viewed uncertainty as a "background" mediator with insufficient conceptualization and measurement. To better understand the nature of uncertainty and mitigate its potentially harmful effects on B2C e-commerce adoption (especially for important purchases), this study draws upon and extends the principal-agent perspective to identify and propose a set of four antecedents of perceived uncertainty in online buyer seller relationship superceived information asymmetry, fears of seller opportunism, information privacy concerns, and information security concerns which are drawn from the agency problems of adverse selection (hidden information) and moral hazard (hidden action). To mitigate uncertainty in online exchange relationships, this study builds upon the principal agent perspective to propose a set of four uncertainty mitigating factor-trust, website informativeness, product diagnosticity, and social presence-that facilitate online exchange relationships by overcoming the agency problems of hidden information and hidden action through the logic of signals and incentives. The proposed structural model is empirically tested with longitudinal data from 521 consumers for two products (prescription drugs and books) that differ on their level of purchase involvement. The results support our model, delineating the process by which buyers engage in online exchange relationships by mitigating uncertainty. Interestingly, the proposed model is validated for two distinct targets, a specific website and a class of websites. Implications for understanding and facilitating online exchange relationships for different types of purchases, mitigating uncertainty perceptions, and extending the principal-agent perspective are discussed.

The Specificity of Environmental Influence: Socioeconomic Status Affects Early Vocabulary Development Via Maternal Speech

Abstract: The hypothesis was tested that children whose families differ in socioeconomic status (SES) differ in their rates of productive vocabulary development because they have different language-learning experiences. Naturalistic interaction between 33 high-SES and 30 mid-SES mothers and their 2-year-old children was recorded at 2 time points 10 weeks apart. Transcripts of these interactions provided the basis for estimating the growth in children’s productive vocabularies between the first and second visits and properties of maternal speech at the first visit. The high-SES children grew more than the mid-SES children in the size of their productive vocabularies. Properties of maternal speech that differed as a function of SES fully accounted for this difference. Implications of these findings for mechanisms of environmental influence on child development are discussed. Family socioeconomic status (SES) is a powerful predictor of many aspects of child development. An aim of current research is to identify the pathways by which SES exerts its well-established influence (DeGarmo, Forgatch, & Martinez, 1999; Keating & Hertzman, 1999; Linver, Brooks-Gunn, & Kohen, 2002; National Research Council and Institute of Medicine, 2000). Because SES and child development are multifaceted variables and because many factors that influence child development covary with SES, the causal relations underlying SES effects on child development may be difficult to uncover (Hoff, Laursen, & Tardif, 2002). The present study focused on one reliably observed relation between SES and child development and sought to identify the underlying mechanism. The relation in focus is that between SES and early