Anti-American sentiment and America's perceived intent to dominate: An 11-nation study

Perceptions of America as a powerful but malevolent nation decrease its security. On the basis of measures derived from the stereotype content model (SCM) and image theory (IT), 5,000 college students in I I nations indicated their perceptions of the personality traits of, intentions of, and emotional reactions to the United States as well as their reactions to relevant world events (e.g., 9/11). The United States was generally perceived as competent but cold and arrogant. Although participants distinguished between the United States' government and its citizens, differences were small. Consistent with the SCM and IT, viewing the United States as intent on domination predicted perceptions of lack of warmth and of arrogance but not of competence and status. The discussion addresses implications for terrorist recruitment and ally support.

Introducing employee social identification to customer satisfaction research: a hotel industry study

The purpose of this paper is to address the concept of linkage research and propose the addition of social identity theory as an important consideration in managing employee-customer interactions and customer satisfaction.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.