Artificial intelligence (AI) has provided significant help in many fields of life. This study proposed a framework that helped in understanding customers’ attitudes about the adoption of Robo-advisors. The role of the Technology Readiness Index moderated as one of the primary relationships. A total of 208 potential users of Robo-advisor services provided the data that confirmed the validity of the model. This model provided the input for structural equation modeling and analysis of the study hypotheses. The results indicated that consumers showed positive attitudes about Robo-advisor services, with the moderating effect of Technology Readiness Index dimensions, namely, contributors and inhibitors. Perceived ease of use, perceived usefulness, and perceived convenience influenced consumers in developing positive attitudes about this service. Financial businesses can design better AI Robo-advisor services to fulfill the requirements of a wide range of consumers. This proposed framework contributes to the consumers’ understanding of behavioral intentions for the use of Robo-advisors in FinTech.

The rapid development of industrialization, transportation, and urbanization has a negative impact on nature such as increased pollution, decreased pervious surface area, and increased temperatures. Researchers are consistently trying to find out ways that can facilitate the reduction of naturally unfriendly impacts. In this context, “green corridors” play a critical role. The main goal of the “green corridors” is to help build sustainable industrial, urban, and transportation networks by meeting requirements for environmental, technical, economic, social, and space planning issues. This paper provides a critical perspective on extant “green corridor” research by analyzing its performance and unwinding the intellectual structure. The perspective also sheds light on gaps in current literature and, therefore, possible future research avenues and calls for research in the field of “green corridor.”

Psychosocial risks are considered as a burning issue in the Asia-Pacific region. The aim of this study was to investigate the impact of psychosocial work environment factors on health of petrochemical industry workers of Malaysia. In lieu to job demands-resources theory, significant positive associations were found between quantitative demands, work-family conflict, and job insecurity with stress, while a significant negative association of role clarity as a resource factor with stress was detected. We also found that quantitative demands were significantly associated with the mean arterial pressure (MAP). Multistage sampling procedure was used to collect study sample. Structural Equation Modeling was used to identify relationship between the endogenous and exogenous variables. Finally, the empirically tested psychosocial work environment model will further help in providing a better risk assessment in different industries and enterprises.

Robust scientific evidence exists for occupational group of heavy vehicle drivers in oil and gas transport who are infected with acute fatigue. Numerous recent studies relate driver’s perceived stress and fatigue with dangerous driving behaviours during performance of delivery duties. This study investigates the association between driver’s perceived stress on aberrant driving behaviour and determines fatigue to be the mediation factor. Sample size of 378 male drivers from 10 oil and gas transport companies was selected that operate in peninsular west and east Malaysia, who answered adapted version of Driver Behaviour Questionnaire (DBQ) in the conducted survey. Structural Equation Model (SEM) was applied that proved aberrant driving behaviours of heavy vehicle drivers can be predicted through driver’s perceived stress and fatigue. Fatigue is a mechanism that mediates the association amongst driver perceived stress and aberrant driving behaviours. In conclusion, the parameter of fatigue mediates between the perceived stress which is an appropriate prediction tool and consequent aberrant driving behaviour of heavy vehicle drivers. This study suggests abatement in individual stress and recommends fatigue management to alter risky and aberrant driving behaviours in order to encourage and give impetus to safe driving culture in oil and gas transportation sector in Malaysia.

This chapter examines the increasing threats of natural and human-made disasters to social stability and economic development, worsened by climate change, urbanization, and globalization. It highlights the need for effective disaster management, including early detection, risk reduction, preparedness, response, and recovery, to reduce loss of life, property damage, and long-term community impacts. The research stresses the vulnerability of children and marginalized groups, often overlooked in traditional approaches, emphasizing inclusive strategies. A key focus is the role of knowledge hubs that enable real-time information sharing, collaboration, and data-driven decision-making among governments, NGOs, academia, and communities. These hubs improve disaster estimation, response, and recovery, enhancing social and economic resilience. The chapter calls for further research on knowledge hubs as integrated systems to advance disaster management and crisis response.

This chapter explores the transformative role of Artificial Intelligence (AI) in healthcare, tracing its evolution from theoretical concepts to practical applications such as machine learning (ML), deep learning (DL), and artificial neural networks (ANNs). It highlights AI's ability to enhance clinical decision-making, improve resource allocation, accelerate drug discovery, and shift healthcare from a sick-care to a well-care model. The research emphasizes AI's role in early disease detection and personalized treatment, as well as the rise of digital health solutions empowering patient autonomy. Despite financial and infrastructural challenges, AI-driven healthcare promises improved medical outcomes while addressing ethical and societal considerations. This chapter provides a comprehensive overview of AI's impact on modern medicine and its potential to reshape patient care.