This study investigates the optimization of ride-sharing services (RSS) on the ride-hailing service (RHS) providers in Bangladesh. This study employed an explanatory sequential mixed method research design- a qualitative study followed by a quantitative one. Qualitative data were collected through focus group discussions and in-depth interviews with twenty (20) riders and drivers in Bangladesh, and quantitative data were collected from 300 respondents consisting of riders and drivers using a convenience sampling technique. Factor analysis and hierarchical cluster analysis were applied to the data analysis. The qualitative analysis reveals several significant factors associated with RSS and RHS, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, business growth, and security. The quantitative results indicate that using RSS is associated with more significant benefits than RHS in various aspects, including cost efficiency, fare, fuel consumption, traffic congestion, carbon emissions, environmental pollution, employment opportunities, and expansion of the automobile industry. The findings may assist policymakers in understanding how RSS can yield more incredible economic, environmental, and social benefits than RHS by analyzing fare sharing among passengers, carbon emissions, fuel consumption, and the expansion of the vehicle markets etc. Therefore, the government can formulate distinct policies for RSS holders due to their contributions to economic, social, and environmental concerns. While RHS services are available in many cities in Bangladesh, this study considered only Dhaka and Sylhet cities. Thus, future studies can consider more respondents from other cities for a holistic understanding.

The rapid advancement of financial technology (Fintech) has revolutionized the way financial transactions are conducted, with E-payment services becoming increasingly integral to daily commerce. This paper examines consumer perceptions and attitudes towards E-payment services offered by Fintech companies, identifying key factors that influence their acceptance and usage. Employing a quantitative approach, the research integrates quantitative data from surveys and applied SEM (Structural Equation Modelling) through AMOS. Out of 450, 420 respondents have given their views on perceptual preferences and attitudes with the help of SPSS. KMO and Bartlett’s Test are executed to understand and to check the factors for implementing factor analysis further through extractions. Anticipated findings are expected to reveal a spectrum of consumer attitudes shaped by factors such as trust, security, convenience, and technological familiarity. It contributes to the existing literature by providing updated insights into consumer behaviour in the Fintech sector and suggesting actionable strategies for service providers to enhance user engagement and satisfaction. It holds the potential to inform both theoretical frameworks in technology acceptance and practical marketing strategies for Fintech companies aiming to optimize E-payment services for diverse consumer bases.

The advent of the COVID-19 pandemic has precipitated a paradigm shift in education, marked by an increasing reliance on technology and virtual platforms. This study delves into the post-pandemic landscape of Islamic higher education at the State Islamic Institute of Palangka Raya, Central Kalimantan, Indonesia, focusing on students’ readiness, attitudes, and interests toward sustained engagement with e-learning. A cohort of 300 students across all semesters of Islamic Education partook in the investigation. Utilising Structural Equation Modelling, the study gauged students’ preparedness, perceptions, and inclinations toward online learning. Results indicate a general readiness among students for online learning, with a pivotal role attributed to technological devices and internet connectivity. Positive attitudes toward online learning were prevalent, with flexibility and accessibility emerging as significant advantages. Moreover, students showed keen interest in online learning, valuing its technological advancements, affordability, and intellectually challenging nature. These findings highlight the digital transformation of traditional teaching methods among Islamic higher education students, who are typically known for their emphasis on direct interaction in teaching and learning. Their receptivity to innovative learning modalities and adaptability to the digital era’s difficulties highlight the need for educational institutions to leverage this enthusiasm. Comprehensive online learning platforms, robust technological support, and a conducive learning environment are advocated to empower Islamic higher education students in navigating the digital landscape and perpetuating their pursuit of knowledge and enlightenment.

Sustainable development has attracted widespread attention worldwide, and the circular economy has become one of the essential policies of many countries. Small and medium-sized enterprises are important drivers of world economic growth and can significantly impact the environment. Therefore, SMEs are critical players in implementing a circular economy as the basis for creating a sustainable society. Although a wealth of research on SME environmental management issues can be found in the literature, more must be known about the infusion of green practices in SMEs. The primary purpose of this study is to explore the green practice infusion of Taiwanese SMEs, a context that is particularly relevant due to Taiwan’s strong focus on environmental sustainability and its circular economy industrial development policy. Through a questionnaire survey, this study examined the factors that influence green practice infusion behavior in Taiwanese SMEs and the impact of green practice infusion on circular economy performance. The findings show that the relative advantages and compatibility of the circular economy, organizational support, human resource quality, regulatory pressure, and government support significantly impact the green practice infusion of Taiwanese SMEs. The effects of complexity, customer pressure, and environmental uncertainty on SMEs’ infusion of green practices are not statistically significant. Circular economy performance is positively correlated with green practice infusion. This study can broaden the research scope of SMEs’ environmental management and contribute to a deeper understanding of SMEs’ green practice infusion and circular economy.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.