This research looks into the differences in technological practices across Gen-X, Gen-Y, and Gen-Z employees in the workplace, with an emphasis on motivation, communication, collaboration, and productivity gaps. The study uses a systematic literature review to identify factors that contribute to these variations, taking into account each generation’s distinct experiences, communication methods, working attitudes, and cultural backgrounds. Bridging generational gaps, providing ongoing training, and incorporating cross-generational and technology-enhanced practices are all required in today’s workplace. This study compares the dominating workplace generations, Gen-X and Gen-Y, with the emerging Gen-Z. A review of the literature from 2010 to 2023, which was narrowed down from 1307 to 20 significant studies, emphasizes the importance of organizational management adapting to generational changes in order to increase productivity and maintain a healthy workplace. The study emphasizes the need of creating effective solutions for handling generational variations in workplace.

Incest is one of the most serious forms of sexual abuse that occurs between a father and his daughter. It involves a parent committing something forbidden to their own child, which violates moral standards. This incestuous relationship has a significant impact on the survivors’ psychology, body, and emotions, affecting all aspects of their lives. This study explores the long-term effects experienced by individuals in Malaysia who have survived father-daughter incest (FDI). This study conducted in-depth interviews with 11 key persons from several agencies involved in handling FDI cases in Malaysia. The findings reveal that those who experienced FDI frequently suffered long-term issues. It is important for everyone involved in assisting these individuals. This is aligned with the global Sustainable Development Goals (SDGs), particularly Goal 3, which emphasises the value of good health and well-being for all. It also aligns with Malaysia’s MADANI concept, which emphasises protecting and promoting everyone’s human rights. FDI survivors can receive the protection and assistance they require to live healthier and more successful lives by implementing an effective strategy that includes mental health support, powerful laws, and community education.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

The widespread adoption of digital technologies in tourism has transformed the data privacy landscape, necessitating stronger safeguards. This study examines the evolving research environment of digital privacy in tourism management, focusing on publication trends, collaborative networks, and social contract theory. A mixed-methods approach was employed, combining bibliometric analysis, social contract theory, and qualitative content analysis. Data from 2004 to 2023 were analyzed using network visualization tools to identify key researchers and trends. The study highlights a significant increase in academic attention after 2015, reflecting the industry's growing recognition of digital privacy as crucial. Social contract theory provided a framework emphasizing transparency, consent, and accountability. The study also examined high-impact articles and the role of publishers like Elsevier and Wiley. The findings offer practical insights for policymakers, industry leaders, and researchers, advocating for ongoing collaboration to address privacy challenges in tourism.

This study, based on the Theory of Planned Behavior (TPB), aims to explore the entrepreneurial intentions of university students in Shandong Province, China, and analyze the major factors influencing these intentions. Structural Equation Modeling was applied to data collected from 680 students across five universities in Shandong Province. The findings reveal that attitudes, subjective norms, and perceived behavioral control significantly influence the students’ entrepreneurial intentions. Specifically, a positive attitude towards the outcomes of entrepreneurship emerged as the strongest factor influencing their intentions, indicating that positive perceptions and expectations of entrepreneurship significantly enhance students’ entrepreneurial inclinations. Perceived behavioral control also showed a strong influence, suggesting that enhancing students’ self-efficacy and awareness of accessible resources is crucial for fostering entrepreneurial intentions. However, the influence of subjective norms was weaker, which may relate to specific cultural and social environmental factors. This study not only provides an empirical basis for entrepreneurship education and policy-making in Shandong Province and beyond but also offers new insights into the application of TPB in the field of entrepreneurship research.