Mostafa Mohamad

This chapter presents an SLR that offers a critical analysis of the increasing use of Artificial Intelligence (AI) in recruitment, with emphasis on issues of algorithmic bias, transparency, explainability, and ethics. It addresses the rapid growth of AI-based hiring processes, along with existing doubts about fairness & accountability and a lack of empirical research, especially in the UAE context. Following a PRISMA-guided protocol, the SLR compiles peer-reviewed articles retrieved from databases, including Scopus, Web of Science, and Google Scholar, from 2004 to 2026. Both conceptual and empirical articles were reviewed. Our findings emphasise the accelerated efficiency and consistency of the hiring process, but perpetuate historic discrimination, particularly against disadvantaged groups. We found a lack of fair AI-based recruitment models in the UAE and other countries with advanced AI recruitment contexts.

The growing use of intelligent digital technologies is reshaping airport supply chains, but technology adoption alone does not explain why automated baggage handling initiatives succeed. knowledge‐driven supply chain digitalization (KSCD) is defined here as the organizational capability to use connected digital technologies and real‐time data to share information, coordinate decisions, and integrate baggage‐handling activities across airport supply‐chain actors. Fully Automated Baggage Handling System Success (FABHSS) is conceptualized as a multidimensional operational outcome reflected in efficiency, timely and reliable processing, routing accuracy and traceability, cross‐functional coordination, and overall support for baggage operations. Drawing on the Knowledge‐Based View and Dynamic Capability Theory (DCT), the study tested a moderated mediation model using survey data from 220 employees in airport operations, logistics, information technology, engineering, and baggage handling at major international airports in the United Arab Emirates. Covariance‐based structural equation modelling showed that KSCD positively influenced Knowledge Integration Capability (KIC; beta = 0.724, p < 0.001) and FABHSS (beta = 0.321, p < 0.001), while KIC positively influenced FABHSS (beta = 0.486, p < 0.001). Bootstrapping confirmed that KIC partially mediates the relationship between KSCD and FABHSS (indirect beta = 0.352, p < 0.001), and Organizational Digital Readiness strengthened the positive KSCD–KIC relationship (interaction beta = 0.184, p < 0.001). The findings show that automated baggage‐handling success depends on converting digitally generated information into integrated operational knowledge within a digitally ready organization.

Purpose: This paper examines how national and international research collaboration links with universities’ performance and their global rankings in the Gulf Cooperation Council (GCC) region. Design/methodology/approach: The empirical analysis is based on the bibliometric data from SciVal (2019-2024). This allowed us to apply Ordinary Least Squares (OLS) regression modelling to define predictors of research performance indicators and ranking among 114 universities in GCC countries. Findings: We found that international and national collaborations are strong predictors of citation impact, research visibility, and publication quality for ranked universities, while non-ranked universities experience much smaller impact. Originality/value: The paper provides the first comparative statistical analysis linking collaboration patterns with global rankings in the GCC countries and empirical evidence, demonstrating the impact of national/international collaboration on institutional visibility, and highlighting the limitations of existing ranking systems in the region. Practical Implications: This paper will guide university administrators and leadership in establishing national/international collaboration priorities to climb global rankings. Keywords: National and International Collaboration; Research Performance; QS Ranking; Gulf Universities; Gulf Cooperation Council Countries. Citation: Karabchuk, T., Shomotova, A. and Mohamad, M. (2026): National and International Research Collaboration across GCC Universities: Impact on Institutional Research Performance and Global Ranking. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, No. 6, pp. 545-567.

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

In the rapidly evolving landscape of technology and business, artificial intelligence (AI), particularly generative AI (GenAI), is reshaping how we teach and learn business strategy (BS). Westerman demystifies GenAI, categorizes its types, and emphasizes its role in organizational transformation, extending these principles to education. Drawing on 100 qualitative surveys from BS educators in the UK and 20 peer-reviewed journal articles, this chapter examines GenAI's applications in personalized tutoring, course design, and skill development for BS students at both UG and PG levels. Findings reveal GenAI as a tool for “small t” transformations in education, enhancing productivity and preparing learners for strategic decision-making, while also highlighting risks such as hallucinations and cultural resistance. Our paper integrates studies on GenAI in higher education, advocates ethical integration, and emphasizes that GenAI does not replace educators but amplifies their impact, fostering competent leaders in a digital era. Our study develops a framework for absorbative capacity in BS education to foster critical thinking, business transformation, and innovation driven by GenAI.

Purpose: The study examines how environmental awareness relates to sustainable practices in Abu Dhabi and how age, income, and education shape this relationship. Design/Methodology/Approach: The design adopted was a mixed-methods, consisting of a survey of 350 people, ten in-depth interviews and the combination of the two strands to increase validity and provide social insight. Findings: The study showed high environmental awareness reflected in organic buying, water saving, reduced plastic use, and waste sorting, with weak links to demographics but a strong positive link between awareness and sustainable behaviour (B = 0.669, p = 0.001). Practical Implications: The study contributes to the development of a social policy related to sustainable environmental development by developing behavioural programmes and interventions based on artificial intelligence (AI). Originality/Value: The paper presents a new AI-powered-behavioural model and seals a significant knowledge gap in the region by empirically correlating environmental awareness with sustainable behaviour in the Arab Gulf. Keywords: Environmental Awareness; Sustainability; Artificial Intelligence; Abu Dhabi. Citation: Alhrahsheh, R., Ivanova, G. and Mohamad, M. (2026): From Environmental Awareness to Sustainable Action and Social Impact in the age of AI: A Mixed-Methods Study in Abu Dhabi. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, No. 5, pp. 439-456.

Purpose: This study investigates how renewable energy firms utilise Artificial Intelligence (AI)-powered solutions to balance ecological, economic, and operational value in sustainable infrastructure. Design/Methodology/Approach: A hybrid framework was developed, combining Deep Neural Networks (DNNs) and Ant Colony Optimisation (ACO) to train autonomous AI agents. The model was validated using multimodal sensor data from wind turbines, photovoltaic panels, and smart grids. Findings: The framework significantly improved fault detection and maintenance optimisation. Organisationally, swarm intelligence enabled cost-effective resource allocation. Environmentally, the system reduced energy waste and carbon emissions while increasing grid reliability. Originality/Value: This research uniquely integrates swarm intelligence with deep learning, reconceptualising AI as a foundational agent for autonomous energy management and sustainability. Practical Implications: The framework provides a roadmap for energy executives to optimise maintenance, achieve decarbonisation goals, and ensure stability through AI-driven resource management. Keywords: AI Agent; Sustainable Energy; Deep Neural Networks; Predictive Maintenance; Ant Colony Optimisation; Swarm Intelligence; Smart Grids. Citation: Mohamad, M., Kohli, A., Srivel, R. and Najdawi, A. (2026): Novel Integration of Ant Colony Optimisation and Deep Neural Networks in AI Agents for Predictive Maintenance of Sustainable Energy Systems. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, No. 4, pp. 321-342. WASD: London, United Kingdom.

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. Original/value of the paper: 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: Ivanova, G., Alhrahsheh, R. and Mohamad, M. (2026): Artificial Intelligence, Environmental Awareness, and the Future of Sustainable Behaviour: A Sociological Study in Abu Dhabi. World Journal of Science, Technology and Sustainable Development (WJSTSD), Vol. 21, Nos. 1/2/3, pp. 215-230. WASD: London, United Kingdom.

Purpose: We explore how sustainability companies deliver Artificial Intelligence powered (AI-Powered) solutions for Business Model Innovation (BMI) and how they balance between ecological, economic, and social value across diverse industries and company sizes. Design/Methodology/Approach: We adopt qualitative semi-structured interviews with the top 20 sustainability executives and founders in the Netherlands as a unique context of sustainability. Findings: We found differences between large and small sustainability companies from the Technological, Organisational, and Environmental (TOE) perspectives. Technically AI is instrumental for operational efficiency, real-time environmental monitoring, and supply chain traceability. Organisationally, sustainability companies focus on design-led approaches, bottom-up innovation, and AI–strategy alignment. Environmentally, they face challenges such as supply chain collaboration resistance, the impact of regulation, and market readiness. Originality/Value of the Paper: This study offers original multi-case evidence from the Netherlands, providing a systemic framework based on the TOE theory to reconceptualise AI not just as a facilitator but as a foundational input shaping sustainable BMI. Practical Implications: It guides sustainability executives and SMEs’ founders to embed AI efficiently to align with client values. Keywords: Artificial Intelligence; Business Model Innovation; Technological, Organisational, and Environmental Theory; Digital Sustainability; Multiple Case Studies. Citation: Mohamad, M., Balıkel, A. E., Chowdhury, D., Russell, C., and Mahmoud, S. (2026): Artificial Intelligence for Business Model Innovation in Digital Sustainability Companies: Multiple Cases from the Netherlands. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, Nos 1-2, pp. 71-87.