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Food waste by households is increasing rapidly in developing countries like Malaysia, contributing to environmental damage, higher food costs and threaten reliable food supply. Existing research based on the Theory of planned behaviour (TPB) has largely emphasized rational decision making, overlooking the influence of emotions, facilitating conditions, and habits on household food waste behaviour. This study integrates the TPB with the Theory of interpersonal behaviour (TIB) and develops a research framework that incorporates constructs derived from both cognitive and affective dimensions, facilitating conditions, and habits-oriented constructs to predict both the intention to reduce food waste and actual food waste behaviour. Data were collected from 397 Malaysian households via a questionnaire survey, and analysed using PLS-SEM. Results show that food planning, health risk concern, attitude, negative emotions, and subjective norm significantly influence the intention to reduce food waste, with food planning showing the strongest positive effect, while health risk concern has a negative effect. Unexpectedly, perceived behavioural control did not influence intention. As for food waste behaviour, perceived behavioural control and intention were significant positive predictors, whereas being a good food provider has a significant negative effect. Food planning, healthy diet concern, and cost savings do not predict behaviour. These findings suggest policymakers refocus interventions on strengthening public awareness and emotional accountability, enhancing food planning infrastructure, and reshaping social norms around responsible consumption.
Generative artificial intelligence (AI) is now embedded in the production of social media visuals. However, research has concentrated on production processes rather than audience outcomes, and empirical work in the Arab context remains scarce. This study examines how AI-generated visual design is adopted by creative professionals, deployed by institutions, and understood by those practitioners to affect user behaviour. Nine semi-structured interviews were conducted in Bahrain with three purposively sampled groups: content creators, institutional representatives, and specialists. Audience response was therefore accessed through practitioner accounts rather than through direct user data. Interviews were analysed thematically in MAXQDA against a ten-theme framework, yielding 223 coded paragraphs. Three key findings emerge. Adoption is task-contingent rather than scalar, ranging from no reliance on purely graphic work to 80% reliance on institutional video, with time-saving the dominant motivation while claims about creativity are consistently hedged. Verification of AI output is not a single ethical competence but a family of domain-specific capacities derived from each institution's own accountability regime. Practitioners across sectors and languages independently reported that content depicting people outperforms AI-generated alternatives, suggesting that established attention and identification mechanisms constrain substitution. Claims concerning users are practitioner-reported and require corroboration through direct audience research.
IntroductionInformation asymmetry between bank managers and external stakeholders is a common relationship between financial-statement fraud and banking instability.MethodsThis study combines an AI-augmented forensic accounting framework with voluntary disclosure, banking stability indicators, and exploratory machine learning (ML) approaches. The study integrates fixed-effects regression with Logistic Regression, Random Forest, XGBoost, Isolation Forest, and SHAP-based explainability using panel data from the whole population of seven banks listed on the Palestine Exchange (2019–2025).ResultsAccording to the econometric results, there is a conditional rather than a uniform relationship between voluntary disclosure and financial stability, with variation by bank size, age, and leverage. Additionally, the exploratory machine-learning analyses indicate that nonlinear approaches could help find unusual bank-year records and instability-risk patterns that are not fully captured by traditional linear models. SHAP analysis enhanced the interpretability of model classifications, and ensemble approaches outperformed Logistic Regression in cross-validation within this small sample. The machine-learning results are considered as exploratory proof-of-concept evidence rather than externally confirmed predictive outcomes due to the small sample size and lack of independently verified fraud labels.DiscussionOverall, the study shows how AI-augmented forensic accounting can enhance supervisory prioritization, instability-risk screening, and the expert assessment of anomalous observations in institutionally unstable banking contexts, thereby complementing traditional econometric analysis.
Abstract Scarcity of water continues to be among the key challenges facing the Middle East and North Africa (MENA) region, as fast-growing populations and soaring industry place additional strains on already scarce supplies. In this chapter, it is discovered how sustainable water treatment can be achieved from the use of nanotechnology to boost purification effectiveness, lower costs and facilitate environmental conservation. With the use of the Technology–Environment–Society (TES) approach, this chapter clarifies how these innovations converge to facilitate regional advancement. A comparative study was carried out among five MENA region nations – Saudi Arabia, the United Arab Emirates, Egypt, Jordan and Morocco – based on secondary information, government reports and research journals. Results indicate that Saudi Arabia and the UAE spearhead regional development with robust policies, research funding and industry partnerships, and Egypt, Jordan and Morocco indicate slow but growing participation. Outcomes also suggest that filtration and seawater desalination systems based on nanotechnology are able to remove pollutants from the environment to the extent of more than 90% while lowering energy consumption to the level of approximately 20% and thus join the ranks of economic and environmental sustainability. Nonetheless, issues like inadequate funding, poor policy alignment and inadequate awareness among the public continue to inhibit mass adoption. The research finds that incorporating nanotechnology as part of integrated national and regional schemes supplemented with explicit regulation, education and cooperation can rejuvenate MENA’s water sector. It reiterates that sustainable prosperity relies not only on technologies but also on robust administrative capacity and civil engagement.
This study investigates the key determinants of bank stability and profitability in commercial and Islamic banks listed on the Amman Stock Exchange (ASE) in Jordan, with a focus on credit risk and capital adequacy during the period 2018–2024. Using panel data from 15 banks, the study applies fixed effects regression models with clustered standard errors. Liquidity is proxied by the loan-to-deposit ratio (LDR), credit risk by the loans loss provisions-to-total loans ratio, and capital strength by the equity-to-assets ratio, alongside a COVID-19 dummy and an interaction term between liquidity and credit risk. Financial performance and stability are measured using return on assets (ROA), return on equity (ROE), and the logarithmic Z-score. The findings indicate that credit risk has a significant negative effect on both bank performance and financial stability, whereas capital adequacy exerts a positive and significant effect. The COVID-19 pandemic negatively affected financial performance and stability, while liquidity (LDR) shows no significant direct effect. The interaction between liquidity and credit risk was statistically insignificant across all estimated models, suggesting that credit risk remains the dominant determinant regardless of liquidity conditions. The study highlights the importance of effective credit risk management and strong capital buffers in enhancing bank resilience. It contributes to the literature by providing recent evidence from the Jordanian banking sector and by incorporating multiple performance measures, a pandemic shock variable, and risk interaction effects to better understand bank stability within a unified empirical framework for an emerging banking market.
