This study examines the potential of FinTech money lending platforms, as viable alternatives for funding SMEs in Nigeria. The motivation for the study arises from the increasing role of FinTech platforms, when it comes to providing financial services to SMEs and the need to enhance their effectiveness and contributions. The study objectives focus on evaluating the accessibility of funds, user interface, transparency, risk management, regulatory compliance, and reputation of the FinTech lending platforms, in terms of their trustworthiness. Data was collected through focused group discussions with 20 SME owners who have used the lending platforms to access business funds, and content analysis was employed to collect relevant information from secondary data sources. Data collected was analyzed using a theme-based qualitative descriptive analysis approach. Amongst others, findings of the study highlight the importance of trustworthiness in determining SME’s perception of FinTech platforms, as practices like transparent disclosures and customer friendly policies were found to foster trust. Amongst others, this study recommends more collaboration between FinTech platforms and the government, as well as other traditional lending institutions, in order to enhance the effectiveness of their services and build trust amongst SME users.

The Covid-19 pandemic has caused some manufacturing companies to experience a decline in company value. The decline in company value occurs due to various factors. Factors that are thought to affect company value include profitability, liquidity, solvency and dividend policy. This study aims to determine the effect of profitability, liquidity and solvency on company value with dividend policy as a mediating variable. The population and sample in this study are manufacturing companies listed on the Indonesia Stock Exchange during the 2019-2022 period. The research design used is quantitative research. The data used are secondary data obtained through the IDX website and the websites of each company. The data analysis used is descriptive statistical analysis, classical assumption testing and multiple regression analysis.

The purpose of this paper is to analyze the emergence process or a new paradigm in the theory and practice of knowledge management. It is the paradigm of artificial knowledge. This new paradigm changes the semantic spectrum of the concept of knowledge as it has been used so far in knowledge management systems. Artificial knowledge is completely differentiated from human knowledge and can no longer be considered a justified true belief. Artificial knowledge is a product of artificial intelligence technology. The paper performs a semantic analysis of the new concept and its features by comparison with human knowledge. Also, the paper presents a bibliometric analysis of the most significant publications discussing artificial knowledge and artificial intelligence. The bibliometric analysis is done using VOS viewer, a specialized software program for such research. The present paper shows that researchers in knowledge management face the emergence of a new paradigm.