Purpose: Generative Artificial Intelligence (GenAI) weakens the assumed link between authenticated identity and reliable organisational evidence. This conceptual paper reconceptualises digital organisational identity as governance infrastructure that links actors, organisational authority, economic substance, and AI-mediated evidence in business, accounting and assurance. Design/Methodology/Approach: The paper adopts a conceptual methodology that combines critical literature synthesis, comparative analysis, and model construction. Guided by Power’s Audit Society framework, it integrates digital identity, accounting, and AI-governance literature and compares the UK Trust Framework, Italy’s SPID and Poland’s mObywatel to identify key constructs and map their relationships within an identity–evidence trust-chain model. Findings: The model shows that authentication does not establish organisational authority or economic substance; GenAI adds provenance risk by enabling credible-looking evidence to become detached from its underlying event. Original/Value of the Paper: The paper connects previously separate literatures and integrates four evidentiary construct’s identity, organisational authority, economic substance and AI provenance with second-order auditability as a mechanism for preserving accountable professional judgement in business. Research Limitations/Implications: The framework is conceptual and requires empirical validation. Keywords: Generative Artificial Intelligence Digital Organisational Identity Digital Identity Governance Accounting Evidence Audit Society Professional Judgement National Digital Identity Regimes Assurance Citation: Sulimierska, M., Sankar, A. S. and Kocia, A. (2026): Digital Identity in the Age of Generative AI: A Conceptual Comparative Analysis of the UK, Italy and Poland. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, No. 6, pp. 565-580.
Purpose: This study aims to develop a data-driven predictive framework to assess and forecast respiratory morbidity risks associated with air pollution and climate variability across the UAE, addressing the gap in region-specific health-environment models. Design/Methodology/Approach: Using environmental data from the National Centre of Meteorology (NCM) and health data from the Ministry of Health and Prevention (MOHAP), machine learning models, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), will be applied to analyse time-series data integrating air pollutants, namely Particulate Matter ≤ 2.5 μm (PM₂.₅), Particulate Matter ≤ 10 μm (PM₁₀), Nitrogen Dioxide (NO₂), Sulphur Dioxide (SO₂), Ozone (O₃), Carbon Monoxide (CO), together with meteorological factors (temperature, humidity, wind speed). Findings: The expected outcome is an explainable AI system capable of issuing early warnings for high-risk respiratory health days, improving predictive accuracy through multi-factor integration. Original/Value of the Paper: The study contributes an innovative, UAE-specific, explainable AI framework that supports public health resilience, hospital preparedness, and environmental policy under UAE Vision 2031. Keywords: Air Pollution; Machine Learning; Respiratory Health; Predictive Modelling; United Arab Emirates; Explainable Artificial Intelligence; Time-Series Forecasting; Environmental Health Policy Citation: Mohamad, M., Ivanova, G., Kohli, A. and Srivel, R. (2026): Tree-Based Machine Learning Models for Respiratory Health Prediction in the UAE: Integration of Air Quality and Meteorological Data. World Journal of Science, Technology and Sustainable Development, Vol. 21, No. 6, pp. 545-564
Purpose: To explore the moderating effect of digital awareness on the relationship between Artificial Intelligence (AI)-Driven personalisation and sustainable consumer behaviour. Design/Methodology/Approach: A quantitative methodology was adopted; a questionnaire was distributed to (463) consumers in the Middle East and North Africa region (MENA). SPSS was used to screen and process the primary data collected. Findings: Results indicated the acceptance of the study’s hypotheses, arguing that AI-driven personalisation can create sustainable consumer behaviour through the moderating role of level of the digital awareness. Originality/Value: This paper empirically validates the results of previous researchers by demonstrating that increasing digital awareness can dramatically enhance the positive effect of AI personalisation on sustainable consumer behaviour. Research Limitations: Results are limited to MENA consumers. Theoretical implications need longitudinal and multi-region validation. Practical Implications: To make consumer behaviour in MENA sustainable, brands ought to increase the levels of digital literacy among consumers and teach them the credibility of AI-based green recommendations. Keywords: Personalisation; Sustainable Consumer Behaviour; Digital Awareness; Personalisation Accuracy; Perceived Usefulness; Trust of AI Recommendations; Interactivity. Citation: Hashem, T. N., Jaradat, N. M. S., Adili, A. N.B. and Ahmad, A. (2026): Does AI-Driven Personalisation Create Sustainable Consumer Behaviour: Moderating Role of Level of Digital Awareness. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, Nos. 1/2/3, pp. 125-143. WASD: London, United Kingdom.
Purpose: To explore the influence of AI-driven sustainable marketing strategies on consumer acceptance of responsible consumption. Design/methodology/approach: Quantitative methodology was employed through an online self-administered questionnaire completed by 826 consumers from the Middle East and North Africa (MENA) region. Findings: Artificial Intelligence (AI)-driven marketing strategies can increase consumers’ responsible consumption with social proof via micro-influencers having the strongest influence. In addition, verified sustainable purchases are supported by transparent supply chain data. MENA brands can establish trust as more people use digital marketing. Original/Value of the Paper: The paper is the first empirical study of six dimensions that provides a framework of culturally based quantifiable findings that have not previously been considered in the literature. Research Limitations: Restricted to an urban population of consumers in the MENA region and self-reported data, the results can be biased against rural or less digitally literate groups. Practical Implications: Regulators and platforms can use an evidence-based approach to require transparency-enhancing AI tools in digital marketplaces. Keywords: AI Marketing; Personalised Messaging; Sustainability; Eco-Label Verification; Price Optimisation; Micro-Influencers; MENA; Lifecycle Impact Citation: Hashem, T. N., Naim, A., Abualkanam, A. S., Isied, M. M. and Mustafa, S. A. (2026): Influence of AI-Driven Sustainable Marketing Strategies on Consumer Acceptance of Responsible Consumption. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, No. 5, pp. 419-437.
Purpose: This article explores the use of enforceable contracts to advance sustainable development, leveraging innovative legal structures for Sustainable Development Goal (SDG) implementation and addressing global challenges such as inequality, environmental harm, and governance. Methodology: A qualitative doctrinal and comparative study of legal sources considers the enforcement of contracts and how it relates to SDGs. Findings: Contract enforcement that supports sustainability supports SDGs such as Peace, Justice, Climate Action, and Reduced Inequalities, but inconsistent legal standards and limited awareness impede implementation. Originality: Contract enforcement is crucial for sustainable development. Strategies for incorporating SDGs into legal documents guide decision-makers on laws and business strategies. Research Limitations: Using secondary information and changes in the law make it difficult to generalise, underscoring the need to update current guidelines. Practical Implications: Sustainability-related provisions and ethical dispute clauses in contracts encourage fairness, assist vulnerable individuals, and help achieve environmental goals. Keywords: Enforcing Contract; SDGs; Developing Clear Laws; Sustainability Clauses; Preventing Corruption. Citation: Jebarah, S.A. (2026): Enforcing Contracts for Sustainable Development: Aligning Legal Frameworks with SDG Targets. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, No. 4, pp. 287-303. WASD: London, United Kingdom.
