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.

Analyzing network traffic activities is imperative in network security to detect attack patterns. Due to the complex nature of network traffic event activities caused by continuously changing computing environments and software applications, identifying the patterns is one of the challenging research topics. This study focuses on analyzing the effectiveness of integrating Multi-Resolution Analysis (MRA) and visualization in identifying the attack patterns of network traffic activities. In detail, a Discrete Wavelet Transform (DWT) is utilized to extract features from network traffic data and investigate their capability of identifying attacks. For extracting features, various sliding windows and step sizes are tested. Then, visualizations are generated to help users conduct interactive visual analyses to identify abnormal network traffic events. To determine optimal solutions for generating visualizations, an extensive evaluation with multiple intrusion detection datasets has been performed. In addition, classification analysis with three different classification algorithms is managed to understand the effectiveness of using the MRA with visualization. From the study, we generated multiple visualizations associated with various window and step sizes to emphasize the effectiveness of the proposed approach in differentiating normal and attack events by forming distinctive clusters. We also found that utilizing MRA with visualization advances network intrusion detection by generating clearly separated visual clusters.

This study examines the relationship between corporate cash holdings and firm performance within the IT industry, which is characterized by intense competition and rapid technological advancements. We propose an integrated framework that combines principal component analysis (PCA), machine learning (ML) algorithms, and Shapley additive explanation (SHAP) values to estimate and interpret model outcomes. Based on 21,051 corporate financial statement data items from 2004 and 2023, the empirical evidence supports an inverted U-shaped relationship between cash holdings and profitability, suggesting that holding either too little or too much cash is suboptimal. Among the tested models, the random forest demonstrates the highest explanatory power (R²) and the lowest prediction errors (RMSE), outperforming the traditional ordinary least squares (OLS) regression by explaining 47% more variance. Our findings provide practical implications for researchers and stakeholders interested in enhancing corporate risk management and performance.

The objective of this study is to improve the effectiveness of anti-piracy educational strategies by identifying unique digital pirate segments and delivering personalized campaign messages to the target audiences. In the first study, we introduced a segmentation study of digital pirates based on different types of risks involved in pirating activities. We identify four digital pirate segments (anti-pirates, hard-core pirates, performance-sensitive pirates, and finance-sensitive pirates), each demonstrating distinctive characteristics. Further profiling of the segments revealed different risk perceptions regarding gender and piracy experience. In the second study, we conduct an experiment to test the effects of targeted campaign messages for the newly identified pirating segments. Our results show that targeted piracy campaign messages have a significantly higher message persuasiveness, while they damage the attitude towards piracy. However, we found that the targeted piracy campaign messages have a marginal effect on changing the intention to pirate. Findings from this study offer useful implications for the design and implementation of anti-piracy educational campaigns.