Introduction: New energy vehicles (NEVs) refer to automobiles powered by alternative energy sources to reduce reliance on fossil fuels and mitigate environmental impacts. They represent a sustainable transportation solution, aligning with global efforts to promote energy efficiency in the automotive sector. Aim: The purpose of this research is to investigate the influence of social demand on the business model of NEVs. Through a comprehensive analysis of consumer preferences and market dynamics, the research aims to identify strategies for driving the sustainable growth of the NEV industry in respond to societal demands. Research methodology: We conduct a questionnaire survey on 2415 individuals and evaluated that questionnaire data by multifactor analysis of variance to examine individual consumer characteristics. We employed NOVA to evaluate the differences in market penetration factors. Additionally, a regression analysis model is utilized to examine accessibility element’s effects on the consumer’s intensions to buy, addressing categorical and ordered data requirements effectively. Research findings: This research demonstrates that middle-aged and adolescent demographics show the highest willingness to purchase NEV’s, particularly emphasizing technological advancements. Consumer preferences vary based on focus like NEV type, model and brand, necessitating tailored marketing strategies. Conclusion: Improving perception levels and addressing charging convenience and innovative features are vital for enhancing market penetration and sustainable business growth in the NEV industry.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

This study aims to identify the risk factors causing the delay in the completion schedule and to determine an optimization strategy for more accurate completion schedule prediction. A validated questionnaire has been used to calculate a risk rating using the analytical hierarchy process (AHP) method, and a Monte Carlo simulation on @RISK 8.2 software was employed to obtain a more accurate prediction of project completion schedules. The study revealed that the dominant risk factors causing project delays are coordination with stakeholders and changes in the scope of work/design review. In addition, the project completion date was determined with a confidence level of 95%. All data used in this study were obtained directly from the case study of the Double-Double Track Development Project (Package A). The key result of this study is the optimization of a risk-based schedule forecast with a 95% confidence level, applicable directly to the scheduling of the Double-Double Track Development Project (Package A). This paper demonstrates the application of Monte Carlo Simulation using @RISK 8.2 software as a project management tool for predicting risk-based-project completion schedules.

The global agreement on environmentally friendly policies puts pressure on businesses to implement good practices to increase legitimacy in a competitive environment. This research aims to examine business dynamic capabilities and value creation processes through the concept of green dynamic marketing capabilities. This concept addresses the ability of businesses to absorb, manage information and accumulate new knowledge that fuels innovative endeavors. The dynamic capability view and customer value theory are integrated to theoretically explain the value creation process of market-orientated innovative products. A total of 58 global companies in Clean200 were sampled. A quantitative approach was conducted to measure the effect of organizational learning (environment management team, environment management training, environment supply chain management) on green innovation (environmental innovation score, eco design product). The results showed that the contribution of Model-1 (0.473 or 47.3%) explained the effect of organizational learning on environmental innovation score, respectively on the variables of environment management team (2.859/0.005), environment management training (−2.971/0.003), and environment supply chain management (7.786/0.000). The contribution of Model-2 (0.448/44.8%) explains the effect of organizational learning on eco-design product, respectively on the variables of environment management team (4.280/0.000), environment management training (−6.401/0.000), and environment supply chain management (7.910/0.000). Model-3 tested the structural association variables in organizational learning and green innovation. A significant influence can be seen with a probability value smaller than 0.05. This research shows that the concept of green dynamic marketing capabilities can be used to explain the ability of businesses in response to the pressure of green global norms through the development of organizational learning towards creation of green innovation product that has impact on market performance. The implication of this research is the creation of new mindset in which green global norms challenge becomes an opportunity for businesses to improve competitiveness.

Many studies have called for more research and increased knowledge about Family Businesses (FB), notably their sustainability. This work aims to reduce this limitation through a narrative literature review and thus contribute to knowledge about FB’s compliance and sustainability design. The results suggest that interest in sustainability practices is growing but still low, and implementation is challenging. This work presents scientific contributions, notably to the Theories of Vision Based on Resources, Dynamic Capabilities, and Stewardship. At the same time, it contributes to the operationalization of FB, as they can design their sustainability practices and compliance strategies similar to those of others. The value of this work culminates in the original proposal of a framework identifying the leading information representative of the main challenges for the sustainability of FB.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.