As energy, water, transport, telecommunications, healthcare, finance and government services migrate to cloud platforms, industrial internet connectivity, remote operations and data-driven automation, Saudi Arabia’s critical national infrastructure is becoming more digitally integrated. The integration enhances operational capability, but also provides routes for ransomware and advanced persistent threats (APTs) to move from information technology to operational technology, disrupt essential services, manipulate trusted identities and exploit supplier relationships. This review integrates recent peer-reviewed evidence on cyber resilience against these threats and interprets it for the Saudi critical-infrastructure context. The choice of a structured integrative review is due to the evidence being across the fields of cyber resilience, ransomware, APT behaviour, industrial control systems, zero trust, situational awareness, risk governance and recovery engineering. The synthesis suggests that prevention alone cannot deliver resilient infrastructure. It calls for service-centric governance, separation of business and operational environments, identity-based access control, threat-informed monitoring, secure and tested recovery, supplier assurance, and continuous adaptation after exercises or incidents. Ransomware and APTs differ in speed and goals, but both take advantage of weak identity, lack of asset visibility, trusted administrative pathways, and poor recovery design. The paper therefore proposes a Critical Infrastructure Cyber-Resilience Loop that incorporates govern, anticipate, withstand, detect, contain, recover and adapt functions around minimum viable service delivery. The review provides a practical framework for Saudi operators and policymakers, and also highlights research gaps in OT-specific resilience measurement, cross-sector dependency modelling, recovery assurance, and empirical evaluation of national cybersecurity controls.
Saudi Arabia is expanding domestic food production while seeking higher industrial productivity, resource efficiency and resilience under Vision 2030. Olive and tomato processing are strategically relevant because both depend on seasonal agricultural inflows, time-sensitive quality preservation and capacity-constrained thermal, separation and packaging operations. This review develops a data-driven production planning framework for smart-factory capacity optimisation in Saudi olive and tomato-paste manufacturing. The study synthesises literature published from 2020 to 2025 on food-process control, digital twins, Industry 4.0, predictive maintenance, production scheduling and process analytical technologies, complemented by official Saudi agricultural and industrial evidence. The review distinguishes nominal equipment capacity from effective, quality-constrained and utility-constrained capacity, and shows why seasonal factories require planning models that jointly represent raw-material arrivals, product deterioration, changeovers, sanitation, equipment condition, energy and water availability, yield uncertainty and market demand. The proposed framework combines industrial sensing, manufacturing execution data, short-horizon forecasting, digital-twin simulation, finite-capacity scheduling and feedback from advanced process control. It recommends a rolling planning architecture in which forecasted olive and tomato arrivals are converted into feasible campaigns, bottlenecks are protected by predictive maintenance, and process set-points are coordinated with throughput and quality targets. For Saudi manufacturers, the framework links local agricultural scale with industrial localisation by prioritising interoperable data, measurable overall equipment effectiveness, seasonal scenario planning and staged investment rather than technology acquisition without an operating model. The review concludes that smart capacity optimisation can increase usable throughput, reduce avoidable losses and improve resource productivity, but only when planning, process control, maintenance and quality decisions are integrated around a common data model.
Fuel retail stations in Saudi Arabia are positioned at the intersection of energy efficiency, distributed renewable energy, transport electrification, and service-sector modernization. This review develops a strategic framework for transforming conventional stations into energy-efficient mobility and service hubs aligned with Saudi Vision 2030. A structured integrative review synthesizes thirty verified peer-reviewed studies spanning Saudi energy policy, hot-climate building efficiency, photovoltaic performance, electric-vehicle charging, battery storage, and digital energy management. The synthesis indicates that transformation should follow an efficiency-first sequence: establish measured baselines, reduce avoidable cooling and auxiliary loads through weather- and season-responsive operation, deploy climate-adapted photovoltaic generation, integrate EV charging according to traffic demand and electrical hosting capacity, add storage only where it resolves a defined constraint, and coordinate assets through interoperable digital energy management. The review highlights that Saudi climatic conditions make cooling efficiency, seasonal thermostat control, photovoltaic soiling, thermal derating, and maintenance central to lifecycle performance. The framework therefore treats local weather as an operating input: during milder periods, particularly where central and eastern locations experience cooler winter conditions, convenience-retail HVAC should automatically reduce unnecessary cooling rather than continue summer-style operation. It also finds that unmanaged fast charging can create avoidable grid and transformer stress, whereas controlled charging, storage, and forecasting can improve flexibility. Two station archetypes and decision tables, together with two graphical frameworks, translate the evidence into implementation logic. Key research gaps concern station-specific benchmarks, monitored solar-canopy performance, charger utilization, battery economics, and field validation of digital controls. The paper concludes that scalable transformation requires outcome-based regulation, measurement and verification, interoperability, and staged portfolio learning rather than isolated technology deployment.
Muhammad Khurram Toor
Glob Acad J Econ Buss; 2026, 8(5): 1514-1525
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.040
Generative artificial intelligence is causing visual communication to take on an interactive character in which both people and computational systems jointly define, develop, test, and enhance creative possibilities. This development is significant for Saudi organisations since visual communication plays a key role in expressing brand identity, narrating service stories, influencing the customer experience, and gaining institutional legitimacy, even though relevant evidence is still dispersed across various fields such as management, marketing, information systems, advertising, and design research. This review gathers thirty peer-reviewed journal studies that were published between 2020 and 2025 and offers an integrative framework for human-AI collaboration in the areas of visual communication and creative strategy. By means of a structured integrative review and thematic synthesis, the study examines the roles of collaboration, the creative outcomes, the organisational capabilities, the governance requirements, and the implications within the Saudi context. The synthesis identifies five recurring mechanisms: humans providing strategic framing, AI carrying out generative expansion, humans taking the lead in curation, validation being based on organisational and cultural criteria, and learning through documented feedback. Even though generative systems can widen the range of ideas, accelerate visualisation, and boost productivity, these advantages do not automatically lead to communication that is distinctive or strategically coherent. Over-reliance on machine suggestions can reduce diversity, while poor delegation, low AI literacy, and insufficient validation can cause errors to be amplified or the brand message to be diluted. The proposed Generative Human-AI Visual Strategy framework regards human judgment as the final authority and views generative AI as a collaborative partner with well-defined boundaries. For Saudi organisations, effective implementation therefore demands involvement from leadership, the capacity to carry out bilingual and cultural interpretation, the introduction of clear review stages, controls over provenance, the development of the necessary capabilities, and feedback loops that transform experimentation into organisational learning.
Puthan Veettil Abdulla Mohd Kayoom
Glob Acad J Econ Buss; 2026, 8(5): 1548-1558
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.043
Large Saudi corporations operate with substantial capital commitments, complex supplier and customer networks, exposure to commodity and interest-rate shocks, and increasingly digital finance functions. These conditions make cash flow optimization and liquidity risk reduction a dynamic decision problem rather than a static ratio-management exercise. This review synthesizes recent research on corporate cash holdings, working capital, trade credit, financial forecasting, machine learning, explainable artificial intelligence, and Gulf-region liquidity behavior to develop an integrated view of intelligent financial decision models. Evidence indicates that advanced forecasting can improve the timing and granularity of liquidity estimates, but prediction alone does not determine an economically optimal action. Effective models must connect forecasts to working-capital levers, liquidity buffers, financing choices, scenario constraints, and governance controls. Saudi and Gulf evidence further shows that cash policies respond to financial constraints, macroeconomic weakness, policy uncertainty, geopolitical risk, oil-price volatility, Shariah-related financing structures, and sector characteristics. The review therefore proposes a layered decision architecture that combines transaction-level data, probabilistic cash forecasting, optimization, stress testing, explainability, and managerial override. The synthesis argues that the most useful corporate treasury systems should optimize expected liquidity while preserving resilience under adverse scenarios and should measure forecast value by decision outcomes rather than prediction accuracy alone. Research priorities include Saudi-specific benchmarking, causal evaluation of treasury interventions, multi-entity liquidity optimization, interpretable reinforcement or prescriptive learning, and governance standards for model risk. The resulting framework offers a practical research agenda for large Saudi corporations seeking to reduce idle cash, shorten the cash conversion cycle, improve funding decisions, and protect operational continuity without creating opaque automated financial control.
Vibin James
Glob Acad J Econ Buss; 2026, 8(5): 1537-1547
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.042
Enterprise networks are evolving from relatively static connectivity fabrics into distributed computing systems that span campuses, data centers, clouds, edge nodes, remote users, software-defined overlays, and programmable transport. This heterogeneity makes manual configuration and reactive operations increasingly inadequate. This review synthesizes recent research on artificial intelligence for network automation and develops an AI-powered intelligent network automation framework for future-ready enterprise computing systems. The review integrates evidence from machine learning for networking, intent-based networking, zero-touch management, network telemetry, network digital twins, programmable data planes, anomaly detection, federated learning, and explainable AI. The proposed framework organizes automation as a governed closed loop that translates business intent into machine-verifiable objectives, collects multi-layer telemetry, constructs contextual network state, applies predictive and decision models, validates actions in a digital-twin or policy sandbox, executes changes through software-defined and programmable infrastructure, and continuously verifies outcomes. The synthesis shows that effective autonomy depends less on a single algorithm than on the coordination of data quality, model lifecycle management, policy constraints, observability, security, and human oversight. Key research gaps concern cross-domain state representation, safe reinforcement learning, explainability, concept drift, trustworthy intent translation, multi-vendor interoperability, energy-aware optimization, and methods for measuring automation risk. The review contributes a practical architecture, an evaluation logic, and research directions for enterprises seeking higher resilience, agility, and computing efficiency without surrendering operational accountability.
Arshad Pasha Mohammed
Glob Acad J Econ Buss; 2026, 8(5): 1481-1491
DOI : https://doi.org/10.36348/gajeb.2026.v08i05.037
Saudi Arabia’s digital-government agenda increasingly depends on decisions that must be fast, consistent, explainable and adaptable rather than merely digitised. This review examines how artificial intelligence, business rules and case management can be combined into an intelligent decision-automation architecture suitable for public services operating under Vision 2030. The paper synthesises peer-reviewed literature published from 2020 to 2025 across five intersecting streams: artificial intelligence in public administration, Saudi digital transformation, explainable and responsible artificial intelligence, hybrid decision management, and data-driven case management. The evidence indicates that artificial intelligence is strongest when used for prediction, classification, prioritisation and recommendation; business rules are strongest when policy, eligibility, thresholds and legal constraints require deterministic control; and case management is strongest when services involve incomplete information, exceptions, appeals and human judgement. Treating these capabilities as substitutes creates avoidable governance and operational risks. The review therefore proposes a layered framework in which trusted data and case context feed a decision-orchestration layer that coordinates artificial intelligence models, executable rules and case logic under human oversight. A seven-stage lifecycle—sense, validate, decide, explain, authorise, execute and learn—connects model outputs to accountable service actions and continuous improvement. The framework emphasises versioned policies, traceable evidence, appealability, bias monitoring, privacy controls and public-value metrics. The study contributes a practical architecture and governance model for Saudi public entities seeking to automate high-volume decisions without sacrificing legal compliance, institutional accountability or citizen trust.
Top Editors
Dr. Sumathi Kumaraswamy
Associate Editorial Board
Assistant Professor Department of Economics and Finance College of Business Administration University of Bahrain Email: skumaraswamy@uob.edu.bh
Dr. Jitender Kumar
Associate Editorial Board
Assistant Professor, Department of Management Studies, Deenbandhu Chhotu Ram University Murthal, Sonepat, Haryana, India Email: jitenderkbatra@gmail.com
Omer Allagabo Omer Mustafa
Associate Editorial Board
Assistant Professor of Economics, Banking and Finance, Deputy Secretary of Academic Affairs-Sudan Academy for Banking and Financial Sciences (SABFS), Khartoum, Sudan E-mail: omergabo78@sabfs.edu.sd
Dr. Zahoor Ahmad Parray
Associate Editorial Board
Department of Management Studies, South Campus, University of Kashmir Anantnag, Jammu and Kashmir, India Email: zahoorhs@gmail.com
Dr. Hafnida Hasan
Associate Editorial Board
Assistant professor in Applied Sciences University (ASU), Kingdom of Bahrain Email: hafnida.hasan@asu.edu.bh
Dr. Ulysses J Brown III
Associate Editorial Board
College of Business Savannah State University 3219 College Street Savannah GA 31404 Email: brownu@savannahstate.edu , brownuj@gmail.com
Dr. C. Vijai
Associate Editorial Board
Assistant Professor Department of Commerce St.Peter’s Institute of Higher Education and Research, Avadi, Chennai-600 054 Tamil Nadu, India Email: Vijaialvar@gmail.com
Prof. Mounir BELLOUMI
Associate Editorial Board
College of Administrative Sciences, Najran University, Saudi Arabia Email: mounir.balloumi@gmail.com
Prof. P Malyadri
Editor-in-Chief
ICSSR Senior Fellow Centre for Economic and Social Studies (CESS), Begumpet, Hyderabad-500016, India Email: drpm16@gmail.com
Husam Alharahsheh
Executive Editor
Lecturer in business management, University of wales Trinity Saint David - UK Senior lecturer, Several UK and EU based higher education institutions Email: husam.helmi1@yahoo.com
Dr. Mohd Shahril Bin Ahmad Razimi
Deputy Chief-Editor
Programme Coordinator Senior Lecturer, Islamic Business School Universiti Utara Malaysia Email: shahril@uum.edu.my
Aliashraf Ahmadian
Associate Editorial Board
Marketing lecturer Graduated in the Degree of Doctor of Philosophy (phD) from University of Tehran, Iran. Email: AliAshrafAhmadian@ut.ac.ir
ZULFAN NAHRUDDIN
Executive Editor
Lecturer at Muhammadiyah University of Makassar email:- zulfan.nahruddin@unismuh.ac.id
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