This study aims to advance understanding of the factors affecting Generation Z employee commitment in the workplace of the information and technology (IT) companies in Vietnam. A survey of 450 Generation Z employees in IT companies shows that company remuneration, reward and welfare, work environment, colleagues, direct manager, promotion, job characteristics, green initiatives are positively related to Generation Z organizational commitment. More specifically, work environment and direct manager have the highest effect on Generation Z employee commitment to organization while promotion and colleagues have the lowest effect on Generation Z employee commitment to organization. Research results also revealed that green initiatives of the organization have significant effect on Generation Z employee commitment in companies. This finding suggests that including green initiatives in corporate strategy is a valuable approach for improving Generation Z employee commitment to organization. We discuss the implications for theory, practice, limitations, and directions for future research.

A precise risk assessment in a production line constitutes a significant item to identify susceptible areas where there is a possibility of product quality degradation. This also applies to the precast concrete production line in Indonesia that has a spun pile product. Based on a risk assessment activity conducted in this study, it is proposed to build a traceability model in order to maintain and even improve the spun pile product quality in Indonesia. The approach used was the Neural Network of the perceptron model for weighing and will result in a defined traceability path in the context of reducing defects and even failed spun pile products. The simulation result showed that the model has been able to detect risky path possibilities to reduce product quality. The accumulation result of high-risk and medium-risk paths in this study showed that closer to product finalization, the risk will be higher. It is evident that when assessing Indicators, the order from the highest accumulation value first is Curing & Demolding and Stressing & Spinning at 29% each, Casting at 14%, Forming & Setting at 14%, and lastly Cutting & Heading at 14%. Regarding the risk assessment for activities, the first position is Curing & Demolding and Stressing & Spinning with 30% each, the second is Casting and Forming & Setting with 15% each, and the third is Cutting & Heading with 10%.

The menace of road traffic accidents (RTAs) has become a major constraint to development in most developing countries because of driving behaviour. This study examines the effects of road users’ education programmes on driving behaviour toward RTA reduction in Nigeria. Data for the study were collected by random sampling of 287 respondents. The respondents comprising road safety officers and drivers were selected at six (6) zonal headquarters of the Federal Road Safety Commission. The questionnaire presented seventeen (17) statements in a 5-point Likert scale for the respondents to rank in order of importance as they have influenced driving behaviour. The data collected were analysed using exploratory factor analysis to identify the most significant effects of road user education on driving behaviour. The study found that road user education programmes have influenced driving behaviour by improving bad driving acts, maintaining good vehicle conditions, and obeying road communication signs. The finding implies that appropriate driving behaviour will reduce road traffic accidents.

This study investigates university students’ understanding of the mole concept and its implications for chemistry education, highlighting the critical role of mathematical education. A questionnaire was administered to 303 students from universities in Panama, Mexico, Cuba, Chile, and Spain. The results reveal that only 29.7% of participants recognize the mole as a fundamental unit, while 20.8% confuse the amount of substance with a non-existent “Chemical System.” Only 18.5% correctly identified the substance quantity symbol as “n” and 32.7% were aware that Wilhelm Ostwald introduced the term mole, indicating deficiencies in historical knowledge. The significance of these findings highlights major misconceptions and gaps in both conceptual understanding and historical knowledge, underscoring the urgent need for revised teaching strategies. Addressing these issues is crucial for bridging the gap between theoretical knowledge and practical application, thereby enhancing instructional methods and optimizing chemistry education to improve students’ comprehension of fundamental concepts.

Presently, any development initiatives without considering sustainability can barely be imagined. There has been a paradigm shift in the focus of the development partners from the mere development to sustainable development. However, the role of development partners in bringing sustainability in livelihood assets of the rural community has long been questioned. Hence, this study aims to explore the sustainability in the form of changes in livelihood assets of a local community in Bangladesh. This study considers the changes in livelihood assets of the community over the three-time frames - before, during, and after a project implemented by a national NGO called ‘UST’ and subsequently identifies the community’s capacity to sustain the project outcomes after the completion of the project. ‘Sustainable Livelihood Framework (SLF)’ developed by Department for International Development (DFID) was utilized in this study to analyse the vulnerability and livelihood issues of the community members. Data has been collected through focus group discussions, household survey and key informants’ interviews from three distinct villages of ‘Khutamara’ union in the ‘Nilphamari’ district of Bangladesh. The finding of the study states that all the livelihood assets such as the social capital, human capital, natural capital, financial capital, physical capital have positively changed due to the interference of the development partners. This study further finds that even after the completion of project tenure, such positive trends continue to exist among the community members indicating sustainable development. Moreover, political capital- a new type of livelihood has also emerged because of the project implementation which was not quite evident before the inception of the project. In addition, this study explored the unique phenomenon of the Shabolombee Gram, where the transformation altering farmers’, livelihoods does not come from the government or the private sector but originates from a Non-Governmental Organization (NGO). Therefore, the government and its development partners may adopt and incorporate the Modified Sustainable Livelihood Framework (MSLF) to ensure the sustainable development.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.