Global University Systems

Purpose This study investigates the impact of ethical leadership on organizational performance among Malaysian ICT SMEs, focusing on the mediating role of leadership effectiveness. It explores how leadership behaviors, typically defined in Western contexts, function within a non-Western, emerging economy setting. Design/methodology/approach The study incorporates a thorough literature review to conceptualize ethical leadership as a one-dimensional construct and positions leadership effectiveness as an emerging paradigm. Organizational performance is evaluated based on growth and profitability metrics. Data were collected from 675 SMEs in the ICT sector, and the proposed model was rigorously tested using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. Findings Results indicate a positive relationship between ethical leadership and organizational performance, and similarly between leadership effectiveness and organizational performance. Moreover, leadership effectiveness was found to mediate the link between ethical leadership and organizational performance. Practical implications The findings highlight the value of ethical leadership coupled with a focus on leadership effectiveness as strategies for enhancing organizational performance. These insights offer actionable guidance for SMEs aiming to improve performance and contribute to broader economic growth. Originality/value This study contributes to SME literature by integrating the RBV with leadership research and demonstrating how ethical leadership, as an intangible resource, drives performance in emerging markets. The use of a Western-developed measurement tool, adapted to a Southeast Asian context, adds methodological novelty and cross-cultural insight. This study contributes a novel leadership-performance model for SMEs in emerging economies, advancing both ethical leadership theory and practical leadership development in ICT service sectors.

This chapter explores the pivotal role of research and development (R&D) in driving economic growth and diversification, with a particular focus on the Gulf States and a comparative analysis of various global economies. It begins by examining the historical reliance of the Gulf States on oil and their current transition towards innovation-driven economies. The chapter outlines key government initiatives in Saudi Arabia, the UAE, and Qatar, evaluating their impacts on economic diversification and growth. The analysis then shifts to a comparative study of R&D investment and its effects on GDP growth across different regions. It covers Southeast Asia, East Asia, North America, and Southern Europe, offering insights into how government policies and funding mechanisms influence innovation and economic performance. By contrasting the experiences of countries like Singapore, Australia, Japan, China, Mexico, the United States, Canada, Spain, France, and Portugal, the chapter highlights successful strategies and common challenges.

Businesses are integrating artificial intelligence (AI) into their business processes, and this integration will usher in a transformative era over the next five years, thus reshaping the landscape of industries worldwide. The research aims to explore AI's impact on businesses, encompassing efficiency gains, strategic decision-making, and innovation. AI aims to streamline operations, automate routine tasks, and enhance productivity; therefore, organizations are embracing AI-driven analytics to help gain the ability to extract valuable insights from vast datasets. This helps with their data-driven decision-making and helps them gain a competitive edge. The study aims to explore the challenges and opportunities that AI integration presents in the next five years. These include workforce adaptation, ethical considerations, and the potential disruptions to traditional business models. It aims to anticipate that there will be a shift towards collaborative human-AI workflows, where AI augments human capabilities instead of replacing them.

This paper examines the transformative impact of artificial intelligence (AI) on pharmacogenomics, signaling a paradigm shift in personalized medicine. With a focus on enhancing drug response prediction and treatment optimization, AI, particularly machine learning and deep learning algorithms, navigates the complexity of genomic data. By elucidating intricate relationships between genetic factors and drug responses, AI augments the identification of genetic markers and contributes to the development of comprehensive models. The review emphasizes AI's role in guiding treatment decisions, minimizing adverse reactions, and optimizing drug dosages in clinical settings. Ethical considerations, challenges, and future directions are also discussed. This work underscores the synergy of AI and pharmacogenomics, offering a more effective and patient-centric approach to drug therapy, marking a significant advancement in the field of personalized medicine.

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

In this paper, a method to improve the state feedback controller design is proposed for a nonlinear system. The main idea of this paper is providing a desired and uniform performance for all operating point of a Twin-Rotor helicopter model, a nonlinear system with fast and unstable dynamics. In the first step of controller design the possibility of using the method for all of linearized models is investigated that shows the necessity of general controllability and observability around whole possible operation points of system. Although the introduced method is only applicable for a special class of nonlinear systems and needs more considerations in controller parameters tuning, it drastically improves the performance of such a nonlinear system and provides more robustness against disturbances and fast changing in dynamics of system.

Abstract In low- and middle-income countries, migration for work is common but the health consequences on adolescents is poorly understood. Particularly in low-resource settings, when children enter their adolescent years, their lives change dramatically as they take on adult roles. Young girls may get married and forced to move to their husband's home where they conduct domestic work and are exposed to high levels of air pollution from cooking. They are also more likely to become parents themselves. Many girls and boys drop out of school to work near their home or migrate within the country or abroad. They face risks throughout the journey and often work in precarious forms of labour. Among them, the unaccompanied adolescents face even greater risks than those who move with their parents. When parents migrate, despite increases in wealth from remittances, child and adolescent mental and physical health tends to worsen. With evidence mostly from the South Asian context, Dr Devakumar will explain detrimental impact of parental migration on left-adolescents and raise the importance of policy makers and health-care professionals on taking action to improve the health planning targeting of these young people.