Fintech as a three-dimensional phenomenon reflects the rapidly changing technological, financial and business environment. The bibliometric analysis of scientific articles allowed us to identify the main themes and create a map of the field of fintech influences. Systematization of scientific articles revealed the influence of economic development and socio-demographic inequality on fintech development. Government regulatory policies can accelerate the digitisation of financial services and financial inclusion and help the fintech sector face geopolitical challenges. Fintech’s impact was divided into three areas: financial stability and sustainable development, the business ecosystem and human behaviour. The research we summarised allowed us to identify the mechanisms through which fintech influences various fields. A complex approach to the influence of fintech enables us to understand the phenomenon and make better decisions.

The food and beverage sector played a big part in contributing to the economic growth in Malaysia hence there was a major increase in the numbers of restaurants opening up for businesses. This study therefore examines factors with the aims of ensuring a sustainable development in full-service restaurants in West Malaysia. The results of this study have made a substantial contribution to restaurant owner’s’ comprehension of the fundamental components that underlie customer satisfaction and loyalty. By examining the moderating effect of the customer’s gender in full-service restaurants in West Malaysia, the objective of this study was to ascertain the relationships between the three variables (quality of the food served at the restaurant, service quality, and environment), as well as the degree to which each attribute was able to relate to diner satisfaction. The underpinning theory for this study was the Theory of Planned Behavior (TPB). Quantitative methods according to descriptive research and convenience sample strategy were utilized in this cross-sectional study. Questionnaires were distributed to 264 respondents through various online platforms such as WhatsApp, Telegram, Facebook, and email. Data collection was evaluated using the Statistical Program for Social Sciences (SPSS) version 27. In order to examine the connection between the three factors and diner’s satisfaction, various tests such as the multiple regression analysis, One-way ANOVA and Beta Coefficient test were carried out. The findings gave current restaurant owners and potential restaurant owners an overview of the different attributes influencing diner’s satisfaction at full-service restaurants in West Malaysia and also the extent of the moderating effect of diner’s gender had on each attribute. The outcome of this paper is expected to provide a sustainable growth in this industry.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

This study seeks to explore the information value of free cash flow (FCF) on corporate sustainability and investigate the moderating effects of board gender diversity and firm size on the association between FCF and corporate sustainability of Thai listed companies. The dataset consists of companies listed on the Stock Exchange of Thailand (SET) in 2022. Multivariate regression analysis is executed in this study. Subsequently, PROCESS macro served to evaluate the proposed hypotheses. This study found that FCF has a significant positive relationship with corporate sustainability. As well, board gender diversity and firm size both moderate the relationship between FCF and corporate sustainability, such that the positive effect of FCF on corporate sustainability is stronger when the proportion of female boards diminishes, while firm size is smaller. However, when firms have a larger proportion of females on the boards of directors for all levels of firm size, free cash flow indicates that there is no statistically significant effect on corporate sustainability. This study contributes to FCF and sustainability literature by understanding the extent of corporate sustainability.

Using the United Nations’ Online Services Indicator (OSI) as a benchmark, the study analyzes Jordan’s e-government performance trends from 2008 to 2022, revealing temporal variations and areas of discontent. The research incorporates diverse testing strategies, considering technological, organizational, and environmental factors, and aligns with global frameworks emphasizing usability, accessibility, and security. The proposed model unfolds in three stages: data collection, performing data operations, and target selection using the Generalized Linear Model (GLM). Leveraging web crawling techniques, the data collection process extracts structured information from the Jordanian e-government portal. Results demonstrate the model’s efficacy in assessing accessibility and predicting web crawler behavior, providing valuable insights for policymakers and officials. This model serves as a practical tool for the enhancement of e-government services, addressing citizen concerns and improving overall service quality in Jordan and beyond.