This study investigates the relationship between corporate social responsibility (CSR), capital structure, and financial distress in Jordan’s financial services sector. It tests the mediating effect of capital structure on the CSR-distress linkage. Utilizing a panel data regression approach, the analysis examines a sample of 35 Jordanian banks and insurance firms from 2015–2020. CSR is evaluated through content analysis of sustainability disclosures. Financial distress is measured using Altman’s Z-score model. The findings reveal an insignificant association between aggregated CSR engagement and bankruptcy risk. However, capital structure significantly mediates the impact of CSR on financial distress. Specifically, enhanced CSR enables higher leverage capacity, subsequently escalating distress risk. The results advance academic literature on the nuanced pathways linking CSR to financial vulnerability. For practitioners, optimally balancing CSR and financial sustainability is recommended to strengthen resilience. This study provides novel empirical evidence on the contingent nature of CSR financial impacts within Jordan’s understudied financial services sector. The conclusions offer timely insights to inform policies aimed at achieving sustainable and stable financial sector development.

China’s economic structure has made subtle changes with the development of digital economy. Along with the marginal diminishing effect of Chinese monetary policies and the increase of the overall leverage ratio, the Chinese economic growth mode of relying on real estate, trade and infrastructure construction in the past will not be sustainable in the next decade. This paper makes a theoretical analysis on the reduction of the search cost in digital economy. Also, this paper used empirical methods to study the relationship between China’s economic growth and digital infrastructure construction. In conclusion, the digital economy has reduced the search cost for people, and big data will become a product factor participating in labor distribution. In addition, this paper proposes for the first time that digital economy can effectively restrain inflation. The Chinese government needs to attach importance to the issue that current internet enterprise oligarchs will probably monopolize the usage of big data in the development of digital economy in the future and become the obstacle to effective economic growth. In addition, close attention should be paid to the vulnerabilities of financial and taxation systems for digital economic entities to avoid continuous disguised tax subsidies to internet oligarchs, thus preventing industrial monopoly.

The aim of this study is to determine how bank diversification affects bank stability. To this end, it examines data of 136 commercial banks operating in 14 MENA (Middle East and North Africa) countries observed from 2005 to 2021, using the System Generalized Method of Moments (GMM) panel data regression analysis. The selected countries are Bahrain, Egypt, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Morocco, Lebanon, Algeria, Tunisia, Iran, Iraq, and the United Arab Emirates. The main results point to the enhancing effect of income diversification on bank stability. Our results underline the “Bright Side” of banking income diversification in the MENA region. However, this stabilizing income diversification effect is not always maintainable. The results also point to a non-linear relationship between interest/non-interest income and financial stability, suggesting that higher diversification reduces risk. We use a dynamic panel threshold model to determine income diversification thresholds that stabilize banks in the MENA region.

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system's architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.

The rapid rise of live streaming commerce in China has transformed the retail environment, with electronic word-of-mouth (eWOM) emerging as a pivotal factor in shaping consumer behavior. As a digital evolution of traditional word-of-mouth, eWOM gains particular significance in live streaming contexts, where real-time interactions foster immediacy and engagement. This study investigates how eWOM influences consumer purchase intentions within Chinese live streaming platforms, employing the Information Adoption Model (IAM) as theoretical framework. Using a grounded theory approach, this research applies NVivo for data coding and analysis to explore the cognitive and emotional processes triggered by eWOM during live streaming. Findings indicate that argument quality, source credibility, and information quantity significantly enhance consumer trust and perceived usefulness of information, which, in turn, drives information adoption and purchase intention. Furthermore, the study reveals that social interaction between live streaming anchors and audiences amplifies the influence of consumers' internal states on information adoption. This study enhances the Information Adoption Model (IAM) by introducing social interaction as a moderator between consumers' internal states toward live streaming eWOM and their adoption of information, highlighting the value of social interaction in live streaming. It also incorporates information quantity, showing how eWOM quantity affects trust and perceived usefulness. Furthermore, the study contributes to exploring how factors like argument quality, source credibility, and information quantity shape consumer trust and perceived usefulness, offering insights into the cognitive and emotional processes of information adoption in live streaming.