Introduction: The digital era has ushered in transformative changes across industries, with the real estate sector being a pivotal focus. In Guangdong Province, China, real estate enterprises are at the forefront of this digital revolution, navigating the complexities of technological integration and market adaptation. This study delves into the intricacies of digital transformation and its profound implications for the financial performance of these enterprises. The rapid evolution of digital technologies necessitates examining how such advancements redefine operational strategies and financial outcomes within the real estate landscape. The inclusion of government support as a variable in our study is deliberate and stems from its profound influence on shaping the digital landscape. Government policies and initiatives provide a regulatory framework and offer strategic direction and financial incentives that catalyze digital adoption and integration within the real estate sector. By examining the moderating effect of government support, this study aims to uncover the nuanced interplay between policy-driven environments and the financial performance of enterprises undergoing digital transformation. This exploration is essential to understanding the broader implications of public policy on private-sector innovation and growth. Objectives: The primary objective of this research is to evaluate the impact of digital transformation on the financial performance of Guangdong’s real estate enterprises, with a specific focus on return on equity (ROE) and return on assets (ROA). Additionally, this study aims to scrutinize the role of government support as a potential moderator in the relationship between digital transformation and financial success. The research seeks to provide actionable insights for policymakers and industry players by understanding these dynamics. The digital transformation of Guangdong’s real estate sector presents a complex landscape of challenges and opportunities that shape the industry’s evolution. On one hand, the integration of innovative digital technologies into established operational frameworks poses significant challenges. These include the need for substantial investment in new infrastructure, the imperative for a cultural shift towards digital literacy across the workforce, and the continuous demand for upskilling to remain agile in an increasingly digital market. On the other hand, digital transformation affords manifold opportunities. For instance, enhanced operational efficiencies through automation and data analytics offer substantial benefits in terms of cost savings and process optimization. Furthermore, leveraging data-driven insights enables more informed strategic decision-making, which is critical in a competitive real estate market. The capacity to innovate service offerings by tapping into digital platforms and customer relationship management systems also presents a significant opportunity for real estate enterprises to differentiate themselves and capture new market segments. Methods: This study explores the digital transformation of real estate firms in Guangdong, highlighting government support as a critical moderator. Findings show that digital initiatives improve company performance, with government backing amplifying these benefits. Regional disparities in support suggest a need for tailored strategies, indicating the importance of policy in driving digital adoption and innovation in the sector. The study advises firms to leverage local policies and policymakers to address regional imbalances for equitable digital transformation. This study uses a sample of 28 real estate enterprises in Guangdong Province from 2012 to 2022. Panel data analysis with a fixed effects model tests the hypotheses. The study also conducts robustness checks by replacing the key variables. Results: The findings indicate that digital transfo

The aim of this research is to determine the incidence of socioeconomic variables in migration flows from the main countries of origin that form part of the international South-North migration corridor, such as Mexico, China, India, and the Philippines, during the 1990–2022 period. The independent variables considered are GDP per capita, unemployment, poverty, higher education, and public health, while the dependent variable is migration flows. An econometric panel data model is implemented. The tests conducted indicate that all variables have an integration order of I (1) and exhibit long-term equilibrium. The econometric models used, Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS), reveal that unemployment and poverty had the strongest influence on migration flows. In both models, within this international migration corridor, GDP per capita, higher education, and health follow in order of importance.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.

Background: People who are financially literate are able to make sound decisions regarding their money since they have a firm grasp of the fundamentals of money and financial products. The significance of financial literacy has been acknowledged by numerous nations, prompting the formation of task teams to assess their populations and develop educational and outreach programs. The requirement to make educated decisions about ever-increasing financial goods necessitates a higher level of financial literacy. Aim: Being able to make sense of one’s personal financial situation is becoming an increasingly valuable skill in today’s world. One of the most essential components for making sure and successful decisions is having a good grip on one’s financial status. By contrast, financial literacy refers to an individual’s level of knowledge and awareness regarding financial matters, whereas investors’ decision-making is characterised by their understanding, prediction, investigation, and assessment of the various stages and transactions involved in making an investment decision. Risk, a decision-making framework and process, and investing itself are all components of investing. Method: Researchers will conduct a cross-sectional survey of Saudi Arabian investors. We used a structured questionnaire to gather data. Using “Cronbach’s a and confirmatory factors” analysis, we checked whether the data is reliable. The links between financial literacy and investment decisions was demonstrated using structural equation modeling (SEM) in IBM-SPSS and SmartPLS. Purpose: The purpose of this research is to look at how the investment choices of Saudi Arabians are correlated with their degree of financial literacy. Consequently, research on the connection between financial literacy, knowledge, behaviour, and investment choices is lacking. Researchers on this subject have already acknowledged the problem’s importance and intended to devote substantial time and energy to solving it. Findings: The study concluded that there was a significant relationship between financial literacy and financial knowledge with respect of investment decision of investors. Similarly, there was a significant relationship between financial behaviour and financial knowledge with respect of investment decision of investors. The discovery of the outcomes will enable regulatory authorities to aid investors in preventing financial losses by furnishing them with sufficient financial information.

This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

Combining physical, social, and economic elements, urban planning plays a critical role in creating sustainable, resilient, and livable urban environments. It encompasses the regulation of land use, infrastructure, transportation systems, and environmental resources, with a focus on sustainable urban design and green infrastructure. While progress has been made, there are still areas that have not been fully explored, including the integration of renewable energy sources and the development of urban environments that are resilient to environmental stresses. This study aims to analyze the direction and scope of urban planning research and to identify research gaps in this area. The method used is bibliometrics by analyzing data obtained from the Scopus database in January 2024. The results of this study showed that Yufeng Zhang, a professor at Wuhan University, China, was the most productive author in producing publications, namely 22 documents. In addition, the article produced by Qianqian Zhou is also influential in this research topic because it gets a number of citations, as high as 204 citations. Additionally, the results indicate the current focus of research on sustainability, adaptation to climate change, and technology in urban planning. These findings can guide future research, direct policy, and ensure an interdisciplinary approach to modern urban and regional challenges.