This study empirically examines the complex relationship between materialism and economic motivation, proposing an inverted U-shaped relationship. The research analyzes three dimensions of materialism: happiness pursuit, social recognition, and uniqueness, and their impact on economic motivation. The findings suggest that materialism, when balanced, positively influences economic motivation without causing adverse effects. This relationship remains consistent across demographic characteristics and life satisfaction levels, challenging the traditional negative view of materialism. The implications of these findings extend to marketing strategies, policy design, and infrastructure development, offering actionable insights for real-world contexts. This research underscores the importance of balancing materialistic values to foster sustainable economic growth and well-being.

This study explores the marginalization of a poor fishing community in Gwadar, Pakistan. The study provides an insight into how different levels of power, such as hidden, visible/pluralist, and invisible ideological powers, are used in policy arenas to hinder fishers’ access to participatory spaces, decision-making, and resource use. By employing Gaventa’s power cubes analytical model, we analyze fishers’ experiences and prevailing scenarios. Qualitative research methods were used to collect data, including in-depth interviews and participant observation. The finding shows that the interests of the fishing community in fishery policies and ongoing development projects are excluded both with intention and unintentionally. The exclusion of the local fisher community from key spaces brings interruptions and transformations that influence their lives. Due to this, they are induced to join insurgent groups to confront exclusion-based policies in Gwadar, Pakistan.

The study, focusing on Malaysian managers, employs a two-round Delphi research methodology to identify and rank variables influencing their emotional intelligence at work. The research is structured into five key areas, with factors ranked in ascending order of significance. Empathy and emotional resilience are deemed the most important, followed by emotional and self-awareness, work-life balance and stress management, social awareness and relationship management, learning and development, adaptability and continuous improvement, cultural and organizational dynamics, experience, and age. This study sheds light on the variables impacting Malaysian managers’ emotional intelligence skills and provides a ranking of key factors essential for successful development. It not only offers crucial guidance for personal and professional balance but also provides insightful recommendations for understanding and enhancing emotional intelligence skills in the workplace for Malaysian managers and organizations.

During the COVID-19 pandemic, individuals and their families faced various risk factors, which in some cases resulted in divorce. Adolescents in such families had to grapple with COVID-19 across the world, the risk factors faced by adolescents have largely been under-risk factors associated with COVID-19 and divorce. Despite the rise of divorce during studied, especially among adolescents in South Africa. This study aimed to explore the risk factors experienced by adolescents from divorced households during the COVID-19 pandemic and make recommendations for policy and development. This study employed a phenomenological research design in alignment with qualitative research. Purposive sampling was used to recruit five female adolescents in Johannesburg. Data was collected using semi-structured interviews and focus groups. Data was analyzed thematically using Braun and Clarke’s six steps of data analysis. The findings revealed that conflict at home, mental illness, physical and social isolation, a lack of paternal support, and diminished educational performance emerged as risk factors faced by the participants. These findings underscore the need for psychological interventions to help address the risk factors faced by adolescents whose parents divorced during the pandemic and those who face similar circumstances during future crises.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.