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.

Lack of knowledge, attitude, and behavior in managing leftover foods in households impacts the natural ecosystem and food chain, particularly in developing countries. This research aims to analyze appropriate methods for reducing and processing food waste produced in household areas. This research method uses qualitative research with operational research methods carried out for 6 months on 25 housewives in Pondok Labu Village in South Jakarta, Indonesia. The research was carried out in 3 stages, the first stage before the intervention, the second stage providing the intervention, and the third stage after the intervention. Results showed that before the intervention, on average each respondent produced 351 g of food waste each day. This amount decreased to 8.43 g/day after respondents participated in socialization to reduce food waste and training to manage food waste. The concluded that a combination of education and training improves knowledge, attitude, and behavior in household food waste management and helps moderate food waste generation.

This empirical study explores the influence of Hollywood product placements on cultural perceptions and teaching practices of preservice English teachers in higher education in China. Hollywood movies and TV series routinely use product placements as a tactic to blend commercial goals with compelling storylines, which could possibly influence the perceptions, and potential teaching practice of Chinese preservice English teachers. The purpose of this study is to determine the degree to which material culture in the form of product placement in Hollywood affects preservice English teachers’ image of America, and their future teaching practice, altering their expectations and goals as well as how they view the West. The study uses a quantitative study method by means of an online questionnaire (N = 497) and applies structural equation modelling to conduct data analysis. The results find notable significant relationships including those from food, architecture, transportation, and electronic devices to positive image of America, as well as architecture and transportation to potential teaching practice. The most prominent path is from image to teaching. However, certain relationships, including those from fashion to image and food to teaching, do not demonstrate statistical significance. These results contribute to the theoretical and practical understanding of how preservice English teachers see Hollywood’s material culture, and how it affects their perception and possible teaching methods. The findings also demonstrate how preservice teachers’ perceptions and educational approaches are shaped by Hollywood’s material culture in the form of product placement, while simultaneously emphasizing the significance of integration of media literacy and upholding their cultural identity amidst these influences.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.