The aim of this study is to examine the relationship between Environmental, Social and Governance (ESG) activities and the performance of Thai listed firms. The moderating roles of board size and CEO duality on this relationship are also assessed. The ESG score provided by LSEG (formerly Refinitiv) is chosen to measure ESG activities, both as an overall ESG combined scores and as Environment, Social, and Governance pillar scores. Multiple regression analysis is used to test the impact of ESG on firm performance while the PROCESS macro is used to test the moderating effects. Results reveal that the overall ESG combined score demonstrates no statistically significant effect on firm market-based performance. However, it shows the significant effects on firm performance for both the ESG combined score and the Environmental and Social pillar scores when moderated by board size and CEO duality; Governance pillar score exhibits no significant effect. Additionally, it is found that when the CEO operates only as the managing director and small board size and average board size are evident, higher ESG disclosure scores enhance firm performance. However, when the CEO serves as both managing director and chairman of the board of directors, and where there is a large board size, higher ESG disclosure scores diminish firm performance. This study contributes to the ESG literature and encourages companies to enhance their performance by implementing ESG combined activities with good governance policies.

The effects of climate change are recognized globally. This study hypothesizes that climate change impacts are a complex system that creates a ripple effect on water security, food security, and economic security. Ultimately, those domains simultaneously exacerbate climate change effects and produce national security concerns. The study’s framework uses a transdisciplinary team’s quantitative and qualitative approach to evaluate the challenges and possible solutions to climate change security on the Water–Food–Socioeconomic Nexus. Iraq has been taken as a case study highlighting the deficits in management and governance. The dynamic of the ripple effect shows the interventions for each sector’s water-food-socioeconomic and security that collectively impact upon each other over time. The radical shift in the political infrastructure after 2003 from a centralized to a decentralized one without proper preparation is one of the root causes of the governance and management anarchy. About 228 state and non-state actors are involved in decision-making, leaving it fragile and unsustainable. Only 1% of the national budget is allocated to both the Ministry of Water Resources and the Ministry of Agriculture, which leaves no capacity to mitigate the risk of climate change impact.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

Amidst China’s burgeoning population and rapid technological strides, this study explores how elderly citizens navigate and embrace electronic governance (e-governance) platforms. Addressing a crucial gap in knowledge, we delve into their limited digital fluency and its impact on e-governance adoption. Our meticulously crafted online survey, distributed via WeChat across significant cities (Beijing, Shanghai, Tianjin, Changsha), yielded 396 responses (384 analyzable). Utilizing Structural Equation Modeling (SEM), we unearthed key influencers of subjective norms, including perceived ease and usefulness, trust, supportive conditions, and past tech exposure. These norms, in turn, positively shape attitudes. Crucially, educational background emerges as a moderator, amplifying the positive link between attitudes and e-governance engagement intent. This underscores the necessity of an inclusive, customized e-governance approach, offering valuable policy insights and advocating for holistic solutions for older adults. Our research yields empirical and theoretical contributions, paving the way for actionable Social Sustainability Marketing Technologies in China, particularly championing digital inclusivity for seniors.

This study aims to structure guidelines for an intervention model from the perspective of Integral Project Management to improve the competitiveness level of cacao associations in south region of Colombia. The research followed a mixed-method approach with a non-experimental cross-sectional design and a descriptive scope. The study employed a stage-based analytical framework which included: identifying the factors influencing the competitiveness of the cacao sector; grouping these factors under the six primary determinants of competitiveness with reference to Porter’s Diamond Model; and proposing guidelines for an intervention model to enhance the competitiveness of the studied associations through project management. The first stage was conducted via literature review. The second stage involved primary data collected through surveys and interviews with the associations, members, and cacao sector experts in Huila. The third stage entailed grouping the factors within the main determinants that promote and limit the competitiveness of the cacao sector in the context of Porter’s Diamond Model. Based on the analysis of the corresponding restrictive and promoting factors, strategic recommendations were formulated for the various sector stakeholders on the measures that can be adopted to address restrictive factors and maintain promoting factors to enhance and sustain the sector's competitiveness.