The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is due to deep neural networks (DNN) complexity and efficiency in making anomaly detection activities more accurate. However, the complexity of these models makes them black-box models, as they lack explainability and interpretability. Not only is the DNN perceived as a black-box model, but recent research evidence has also shown that they are vulnerable to adversarial attacks. This paper developed an adversarial robust and explainable network intrusion detection system based on deep learning by applying adversarial training and implementing explainable AI techniques. In our experiments with the NSL-KDD dataset, the PGD adversarial-trained model was a more robust model than DeepFool adversarial-trained and FGSM adversarial-trained models, with a ROC-AUC of 0.87. The FGSM attack did not affect the PGD adversarial-trained model’s ROC-AUC, while the DeepFool attack caused a minimal 9.20% reduction in PGD adversarial-trained model’s ROC-AUC. PGD attack caused a 15.12% reduction in the DeepFool adversarial-trained model’s ROC-AUC and a 12.79% reduction in FGSM trained model’s ROC-AUC.

This research investigates the factors influencing the capital structure of 271 non-financial firms listed on the Korean Stock Exchange (KSE) over a broad period from 1995 to 2021, encompassing both stable and crisis conditions. Employing a dynamic panel data model and the generalized method of moments (GMM) estimation, we address the endogeneity issue introduced by the inclusion of lagged dependent variables. Our research integrates firm-specific internal factors with macroeconomic external variables to provide a comprehensive understanding of the influence of varying economic environments on capital structure. Our study suggests that in times of economic stability, the capital structure decisions of a firm are more influenced by internal factors such as profitability. However, in periods of economic downturns, it is the external macroeconomic market conditions that tend to have a greater impact on these decisions. It is also noteworthy that both book leverage (BL) and market leverage (ML) exhibit quicker adjustments during stable periods as opposed to periods of crisis. This indicates a higher agility of firms in adapting their capital structures in stable, normal conditions. Our findings contribute to the existing literature by offering a holistic view of capital structure determinants in Korean firms. They underscore the necessity of adaptable financial strategies that account for both internal dynamics and external economic conditions. This study fills a gap in current research, presenting new insights into the dynamics of capital structure in Korean firms and suggesting a multifaceted approach to understanding capital structure in diverse economic contexts.

AI chatbots are transforming customer service, yet their black-box nature often undermines consumer trust and hinders adoption. Drawing on Explainable AI (XAI), Computers as Social Actors (CASA), and Trust in Automation (TiA) frameworks, this study examines how explainability and anthropomorphism shape consumer trust in AI chatbots and influence adoption across high-stakes (Finance) and low-stakes (Retail) industries. Our findings reveal that explainability strengthens consumer trust, which drives adoption, reinforcing its role in building consumer confidence. Additionally, chatbots that are both explainable and anthropomorphic generate stronger consumer trust, though the influence of anthropomorphism differs by industry. In high-stakes industries, where transparency and reliability are critical, chatbot design should emphasise clear, structured explanations while ensuring that anthropomorphic cues support rather than overshadow trust-building. Conversely, in low-stakes industries, where engagement and interaction quality may take precedence over transparency, anthropomorphic cues play a more significant role in trust formation. Despite these differences, consumer trust remains the strongest predictor of AI chatbot adoption across both industries, highlighting its fundamental role in AI acceptance. This study provides a theoretical framework and practical recommendations for designing AI chatbots that effectively balance explainability and anthropomorphism to enhance consumer trust and adoption in different industry contexts.

This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized Method of Moments (GMM) for estimation, the study addresses endogeneity concerns while exploring the effects of firm-specific and macroeconomic variables on dividend yields. The investigation delineates three distinct economic phases: normal conditions, financial crises, and the aggregate study period, facilitating a granular understanding of firms’ dividend payout adaptability under varying economic landscapes. Empirical findings underscore the persistence of dividend payments, revealing a variable adjustment speed toward target dividend yields contingent upon the economic context, with an expedited adjustment observed during crises. Crucially, firm profitability emerges as a consistent determinant of dividend yields across all examined periods, whereas the influence of macroeconomic variables is notably more pronounced during periods of economic normalcy. This research elucidates the complex interplay between internal corporate strategies and external economic pressures in shaping dividend policies, thereby enriching the discourse on dividend payout behavior in the context of Korea’s economic evolution from an emerging to a developed market.

This paper used complementary panel data models that are fixed effect regression model and panel vector auto regression model. The study was motivated by the hypothesis that both macroeconomic and microeconomic variables have an effect on the loan quality. The first part of the research was to determine the specific macro and microeconomic variables that give rise to the non-performing loans (NPLs) using fixed effect regression model. The empirical findings of this study provide evidence that nonperforming loans depends on macro and micro economic variables, the trend analysis of Zimbabwean commercial banks’ shows an upward movement of over the period of study. The study found out that Gross domestic product (GDP), Inflation, loan deposit ratio and bank size had a statistical significant effect on the level of non-performing loans (NPLs). The second part was mainly to model the dynamic relationship of all the variables that were found to affect non-performing loans (NPLs); this was done through impulse response analysis based on PANEL VAR model. One standard shock to credit growth will be greatly felt in the sixth year, whereas of size of the bank will have a great negative impulse in the seventh year.

This study explores the capital structure determinants of Korean firms over an extensive period (1995-2021), encompassing both stable and crisis conditions. Employing a dynamic panel data model and the Generalized Method of Moments (GMM) estimation, we address the endogeneity issue introduced by the inclusion of lagged dependent variables. Our research integrates firm-specific internal factors with macroeconomic external variables to provide a comprehensive understanding of the influence of varying economic environments on capital structure. Our study suggests that in times of economic stability, the capital structure decisions of a firm are more influenced by internal factors such as profitability. However, in periods of economic downturns, it's the external macroeconomic market conditions that tend to have a greater impact on these decisions. It's also noteworthy that both book leverage (BL) and market leverage (ML) exhibit quicker adjustments during stable periods as opposed to periods of crisis. This indicates a higher agility of firms in adapting their capital structures in stable normal conditions. Our findings contribute to existing literature by offering a holistic view of capital structure determinants in Korean firms. They underscore the necessity of adaptable financial strategies that account for both internal dynamics and external economic conditions. This study fills a gap in current research, presenting new insights into the dynamics of capital structure in Korean firms and suggesting a multifaceted approach to understanding capital structure in diverse economic contexts.