University of North Carolina at Greensboro

About: University of North Carolina at Greensboro is a education organization based out in Greensboro, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 5481 authors who have published 13715 publications receiving 456239 citations. The organization is also known as: UNCG & UNC Greensboro.

Purchase intention of Chinese consumers toward a US apparel brand: a test of a composite behavior intention model

Abstract: Purpose – The purpose of this article is to assist US apparel firms to successfully market in China. It aims to accurately predict Chinese consumers' purchase intentions toward a US brand. For this purpose, it proposes a composite model incorporating two behavioral intention models, Lee's model and decomposed Ajzen's model, and empirically tests the composite model.Design/methodology/approach – A total of 747 consumer data were collected in three cities of China, Shanghai, Beijing, and Guangzhou, utilizing a series of mall intercept method at two levels of shopping malls in each city.Findings – Among the proposed direct paths to purchase intention (PI), attitude toward the US brand apparel was the most important in explaining contemporary Chinese consumers' purchase intentions, followed by external perceived behavioral control (PBC) and subjective norm (SN). Two Confucian values, face saving and group conformity, did not directly affect PI at all, and face saving only influenced PI indirectly through the ...

Labelled data collection for anomaly detection in wireless sensor networks

Abstract: Security of wireless sensor networks (WSN) is an important research area in computer and communications sciences. Anomaly detection is a key challenge in ensuring the security of WSN. Several anomaly detection algorithms have been proposed and validated recently using labeled datasets that are not publicly available. Our group proposed an ellipsoid-based anomaly detection algorithm but demonstrated its performance using synthetic datasets and real Intel Berkeley Research Laboratory and Grand St. Bernard datasets which are not labeled with anomalies. This approach requires manual assignment of the anomalies' positions based on visual estimates for performance evaluation. In this paper, we have implemented a single-hop and multi-hop sensor-data collection network. In both scenarios we generated real labeled data for anomaly detection and identified different types of anomalies. These labeled sensor data and types of anomalies are useful for research, such as machine learning, and this information will be disseminated to the research community.

The Emerging Role of Big Data in Key Development Issues: Opportunities, Challenges, and Concerns

Abstract: This paper presents a review of academic literature, policy documents from government organizations and international agencies, and reports from industries and popular media on the trends in Big Data utilization in key development issues and its worthwhileness, usefulness, and relevance. By looking at Big Data deployment in a number of key economic sectors, it seeks to provide a better understanding of the opportunities and challenges of using it for addressing key issues facing the developing world. It reviews the uses of Big Data in agriculture and farming activities in developing countries to assess the capabilities required at various levels to benefit from Big Data. It also provides insights into how the current digital divide is associated with and facilitated by the pattern of Big Data diffusion and its effective use in key development areas. It also discusses the lessons that developing countries can learn from the utilization of Big Data in big corporations as well as in other activities in industrialized countries.