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.

An Extended Privacy Calculus Model for E-Commerce Transactions

Abstract: While privacy is a highly cherished value, few would argue with the notion that absolute privacy is unattainable. Individuals make choices in which they surrender a certain degree of privacy in exchange for outcomes that are perceived to be worth the risk of information disclosure. This research attempts to better understand the delicate balance between privacy risk beliefs and confidence and enticement beliefs that influence the intention to provide personal information necessary to conduct transactions on the Internet. A theoretical model that incorporated contrary factors representing elements of a privacy calculus was tested using data gathered from 369 respondents. Structural equations modeling (SEM) using LISREL validated the instrument and the proposed model. The results suggest that although Internet privacy concerns inhibit e-commerce transactions, the cumulative influence of Internet trust and personal Internet interest are important factors that can outweigh privacy risk perceptions in the decision to disclose personal information when an individual uses the Internet. These findings provide empirical support for an extended privacy calculus model.

Deep learning applications and challenges in big data analytics

Abstract: Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.

Understanding the Influence of National Culture on the Development of Trust

Abstract: Increasingly, researchers from a variety of business disciplines are finding that trust can lower transaction costs, facilitate interorganizational relationships, and enhance manager-subordinate relationships. At the same time, we see a growing trend toward globalization—in establishing alliances, managing and hiring employees, and entering new markets. These trends suggest a need to view the concept of trust from the perspective of national culture. Drawing on theories from several disciplines, we develop a framework that identifies and describes five cognitive trust-building processes that help explain how trust develops in business contexts. We include a series of research propositions demonstrating how societal norms and values influence application of the trust-building processes, and we discuss implications for theory and practice.

Information privacy research: an interdisciplinary review

Abstract: To date, many important threads of information privacy research have developed, but these threads have not been woven together into a cohesive fabric. This paper provides an interdisciplinary review of privacy-related research in order to enable a more cohesive treatment. With a sample of 320 privacy articles and 128 books and book sections, we classify previous literature in two ways: (1) using an ethics-based nomenclature of normative, purely descriptive, and empirically descriptive, and (2) based on their level of analysis: individual, group, organizational, and societal. Based upon our analyses via these two classification approaches, we identify three major areas in which previous research contributions reside: the conceptualization of information privacy, the relationship between information privacy and other constructs, and the contextual nature of these relationships. As we consider these major areas, we draw three overarching conclusions. First, there are many theoretical developments in the body of normative and purely descriptive studies that have not been addressed in empirical research on privacy. Rigorous studies that either trace processes associated with, or test implied assertions from, these value-laden arguments could add great value. Second, some of the levels of analysis have received less attention in certain contexts than have others in the research to date. Future empirical studies-both positivist and interpretive--could profitably be targeted to these under-researched levels of analysis. Third, positivist empirical studies will add the greatest value if they focus on antecedents to privacy concerns and on actual outcomes. In that light, we recommend that researchers be alert to an overarching macro model that we term APCO (Antecedents → Privacy Concerns → Outcomes).