As an important type of knowledge, standards are key factors in economic development and technological innovation. To analyze the impact of participation in standards development on enterprise performance, this study takes China’s high-tech industry as an example. We use the operating data of listed enterprises in the industry in 2019 and conduct the propensity score matching method matching analysis on the entire sample and the classification. The conclusion shows: From an overall point of view, the participation of enterprises in the development of standards has a positive impact on the enterprise’s return on total assets. Specifically, participating in the development of over three standards can also improve the return on total assets. Large enterprises can increase the return on total assets of the enterprise and the return on invested capital. The state-owned enterprises have a positive effect on the return on total assets of the enterprise. Enterprises in the western, central, and eastern region enterprises can increase their net profit, enterprise value and net profit, return on total assets and enterprise value respectively. The enterprises in Beijing-Tianjin-Hebei region, Guangdong-Hong Kong-Macao Greater Bay Area can improve their return on invested capital and enterprise value, average rate of return respectively. The participation in the development of national standards, industry standards and local standards can help increase their return on total assets, the return on total assets and enterprise value, enterprise value respectively. Finally, we suggestions are put forward to enhance enterprises’ enthusiasm to take part in standards development.

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.

This study uses dynamic capability theory and a resource-based view to examine whether intellectual capital (human, relational, and structural capital) mediates entrepreneurial leadership and innovation success. Drawing on data from 422 senior-level employees working in Peruvian I.T. companies, the proposed relationships were analyzed using SmartPLS 4. Entrepreneurial leadership was found to foster employees’ innovative performance through the mediating role of human capital, relational capital, and structural capital. Practically, businesses often rely on innovation for survival and growth, so they should consider entrepreneurial leadership to create intellectual capital (human capital, relational capital and structural capital) for innovation performance. Businesses should provide entrepreneurial training that emphasizes role modeling intellectual capital and encourages employees to recognize and pursue entrepreneurial opportunities. With significantly limited research, the study contributes by investigating the interrelationship of entrepreneurial leadership, intellectual capital, and innovation performance. The study contributes to the Resource Based View and Dynamic Capability Theory by demonstrating how entrepreneurial leadership contributes to innovation performance through human capital, relational capital, and structural capital.

This study aims to discover the relationship between growth sales, capital structure, and corporate governance on financial performance of energy and basic material sector public companies in Indonesia. Financial performance is observed from 2 aspects: market performance (Tobin's Q) and profitability performance (ROA). The population in this study is firms in the energy and basic material sector on Indonesia Stock Exchange. The total population is 248 firms. 39 firms were selected as samples. The data is obtained from the annual report which starts from the period 2018 to 2022. A total of the population was determined as samples by purposive sampling method. Data analysis using panel data regression. The result shows: 1) Growth Sales have a significant influence on market performance; however, it does not have a significant effect on profitability performance. 2) Capital Structure significantly influences market and profitability performance 3) Corporate governance significantly influences market and profitability performance. Suggestions for companies that must strive to increase sales, maintain good corporate governance and pay attention to the company's capital structure in a balanced manner.

This paper aims to verify the possibility of utilising water-in-diesel emulsions (WiDE) as an alternative drop-in fuel for diesel engines. An 8% WiDE was produced to be tested in a four-stroke, indirect injection (IDI) diesel engine and compared to EN590 diesel fuel. An eddy current brake and an exhaust gas analyser were utilised to measure different engine parameters such as torque, fuel consumption, and emissions at different engine loads. The results show that the engine running on emulsified fuel leads to a reduction in torque and power, an increase in the specific fuel consumption, and slightly better thermal efficiency. The highest percentual increment of thermal efficiency for WiDE is obtained at 100% engine load, 5.68% higher compared to diesel. The emissions of nitric oxide (NO) and carbon dioxide (CO₂) are reduced, but carbon monoxide (CO) and hydrocarbons (HC) emissions are increased, compared to traditional diesel fuel. The most substantial decrease in NO and CO₂ levels was achieved at 75% engine load with 33.86% and 25.08% respectively, compared to diesel.