This study explores factors influencing adoption of mobile banking. Based on extended Technology Acceptance Model (TAM), we identified five factors which influence consumers' behavioral intention to adopt mobile banking: perceived usefulness, perceived ease of use, perceived credibility, perceived self-efficacy, and perceived financial cost. Data was collected from 165 respondents through a survey questionnaire, and the regression was used to analyze the relationships. Our results indicate that all factors except for perceived financial cost have a significant impact on behavioral intention towards mobile banking usage. Perceived usefulness is the most influential factor explaining the adoption intention. We also found that consumers' perceptions are different between mobile banking users and non-users. For users, perceived ease of use is the important factor while perceived self-efficacy significantly influence non-users' adoption intention. Implications from these findings help banking institutions to strategically frame their service model for broader mobile banking adoption.

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The objective of this study is to explore methods to improve the effectiveness of anti-piracy educational deterrence efforts. We studied the effects of message framing (positive vs. negative), issue involvement (high vs. low), risk perception (high vs. low), and message evidence (anecdotal vs. statistical) on the perceived effectiveness of an anti-piracy campaign message. Our experimental results suggest that message frame alone does not have an impact on perceived message effectiveness. However, the effect of message framing is moderated by issue involvement, risk perception, and message evidence. Specifically, a positively framed message is more effective for individuals with low issue involvement, high perceived piracy risk, and who are exposed to anecdotal evidence. In contrast, a negatively framed message is more effective for individuals with high involvement, low risk, and who are exposed to statistical evidence.

Human emotion recognition is critical to people managing their stress and emotions. Although many innovative techniques have been proposed to recognize human emotions, it is still challenging to understand the emotions due to individual differences in the diversity of emotions. This article focuses on analyzing the emotions computationally. In detail, a wavelet transform technique is utilized to extract significant features and find patterns in an emotion dataset. With the extracted features, both classification and visual analysis are performed. For the classification, Logistic Regression, C4.5, and Support Vector Machine are used. Visualization approaches are also utilized to represent similarities and differences among the emotion patterns. From the analysis, the authors found that the proposed method shows an improvement in identifying the differences among the emotions.

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